1
|
Brickman A, Baykara Y, Carabaño M, Hacking SM. Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access. J Pathol Inform 2024; 15:100350. [PMID: 38162951 PMCID: PMC10757022 DOI: 10.1016/j.jpi.2023.100350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/06/2023] [Indexed: 01/03/2024] Open
Abstract
Background Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. Methods WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). Results A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection "Whole Slide Images as Non-fungible Tokens Project" on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. Conclusion Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.
Collapse
Affiliation(s)
- Arlen Brickman
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Yigit Baykara
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Miguel Carabaño
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, United States
| | - Sean M. Hacking
- Department of Pathology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| |
Collapse
|
2
|
Shirvani O, Warnat-Herresthal S, Savchuk I, Bode FJ, Nitsch L, Stösser S, Ebrahimi T, von Danwitz N, Asperger H, Layer J, Meissner J, Thielscher C, Dorn F, Lehnen N, Schultze JL, Petzold GC, Weller JM. Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion. Ann Clin Transl Neurol 2024. [PMID: 39180278 DOI: 10.1002/acn3.52185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/18/2024] [Accepted: 08/10/2024] [Indexed: 08/26/2024] Open
Abstract
OBJECTIVE Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to develop a model that balances minimal input data with reliable predictions of long-term functional independency. METHODS Our study utilized data from the German Stroke Registry on patients with large anterior vessel occlusion who underwent endovascular treatment. We trained seven machine learning models using 30 parameters from the first day postadmission to predict a modified Ranking Scale of 0-2 at 90 days poststroke. Model performance was assessed using a 20-fold cross-validation and one-sided Wilcoxon rank-sum tests. Key features were identified through backward feature selection. RESULTS We included 7485 individuals with a median age of 75 years and a median NIHSS score at admission of 14 in our analysis. Our Deep Neural Network model demonstrated the best performance among all models including data from 24 h postadmission. Backward feature selection identified the seven most important features to be NIHSS after 24 h, age, modified Ranking Scale after 24 h, premorbid modified Ranking Scale, intracranial hemorrhage within 24 h, intravenous thrombolysis, and NIHSS at admission. Narrowing the Deep Neural Network model's input data to these features preserved the high performance with an AUC of 0.9 (CI: 0.89-0.91). INTERPRETATION Our Deep Neural Network model, trained on over 7000 patients, predicts 90-day functional independence using only seven clinical/radiological features from the first day postadmission, demonstrating both high accuracy and practicality for clinical implementation on stroke units.
Collapse
Affiliation(s)
- Omid Shirvani
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Stefanie Warnat-Herresthal
- German Center for Neurodegenerative Diseases, Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Ivan Savchuk
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Felix J Bode
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Louisa Nitsch
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Sebastian Stösser
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Taraneh Ebrahimi
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Niklas von Danwitz
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Hannah Asperger
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Julia Layer
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | - Julius Meissner
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
| | | | - Franziska Dorn
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Nils Lehnen
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Joachim L Schultze
- German Center for Neurodegenerative Diseases, Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Gabor C Petzold
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Johannes M Weller
- Department of Vascular Neurology, University Hospital Bonn, Bonn, Germany
- Department of Neurooncology, University Hospital Bonn, Bonn, Germany
| |
Collapse
|
3
|
Chan VTT, Ran AR, Wagner SK, Hui HYH, Hu X, Ko H, Fekrat S, Wang Y, Lee CS, Young AL, Tham CC, Tham YC, Keane PA, Milea D, Chen C, Wong TY, Mok VCT, Cheung CY. Value Proposition of Retinal Imaging in Alzheimer's Disease Screening: A Review of Eight Evolving Trends. Prog Retin Eye Res 2024:101290. [PMID: 39173942 DOI: 10.1016/j.preteyeres.2024.101290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 08/13/2024] [Accepted: 08/15/2024] [Indexed: 08/24/2024]
Abstract
Alzheimer's disease (AD) is the leading cause of dementia worldwide. Current diagnostic modalities of AD generally focus on detecting the presence of amyloid β and tau protein in the brain (for example, positron emission tomography [PET] and cerebrospinal fluid testing), but these are limited by their high cost, invasiveness, and lack of expertise. Retinal imaging exhibits potential in AD screening and risk stratification, as the retina provides a platform for the optical visualization of the central nervous system in vivo, with vascular and neuronal changes that mirror brain pathology. Given the paradigm shift brought by advances in artificial intelligence and the emergence of disease-modifying therapies, this article aims to summarize and review the current literature to highlight 8 trends in an evolving landscape regarding the role and potential value of retinal imaging in AD screening.
Collapse
Affiliation(s)
- Victor T T Chan
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Siegfried K Wagner
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Herbert Y H Hui
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xiaoyan Hu
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ho Ko
- Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR China; Gerald Choa Neuroscience Institute, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR China
| | - Sharon Fekrat
- Departments of Ophthalmology and Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Yaxing Wang
- Beijing Institute of Ophthalmology, Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Hospital, Capital University of Medical Science, Beijing, China
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
| | - Alvin L Young
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yih Chung Tham
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Pearse A Keane
- NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Dan Milea
- Singapore National Eye Centre, Singapore
| | - Christopher Chen
- Memory Aging & Cognition Centre, National University Health System, Singapore; Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | - Vincent C T Mok
- Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR China; Gerald Choa Neuroscience Institute, Margaret K.L. Cheung Research Centre for Management of Parkinsonism, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Li Ka Shing Institute of Health Science, Lau Tat-chuen Research Centre of Brain Degenerative Diseases in Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, the Chinese University of Hong Kong, Hong Kong SAR, China.
| |
Collapse
|
4
|
Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmol Ther 2024; 13:2125-2149. [PMID: 38913289 PMCID: PMC11246322 DOI: 10.1007/s40123-024-00981-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/15/2024] [Indexed: 06/25/2024] Open
Abstract
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
Collapse
Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań , Poland.
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingxin Zhou
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Meizhu Wang
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Tien Y Wong
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| |
Collapse
|
5
|
Faa G, Coghe F, Pretta A, Castagnola M, Van Eyken P, Saba L, Scartozzi M, Fraschini M. Artificial Intelligence Models for the Detection of Microsatellite Instability from Whole-Slide Imaging of Colorectal Cancer. Diagnostics (Basel) 2024; 14:1605. [PMID: 39125481 PMCID: PMC11311951 DOI: 10.3390/diagnostics14151605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 07/19/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
With the advent of whole-slide imaging (WSI), a technology that can digitally scan whole slides in high resolution, pathology is undergoing a digital revolution. Detecting microsatellite instability (MSI) in colorectal cancer is crucial for proper treatment, as it identifies patients responsible for immunotherapy. Even though universal testing for MSI is recommended, particularly in patients affected by colorectal cancer (CRC), many patients remain untested, and they reside mainly in low-income countries. A critical need exists for accessible, low-cost tools to perform MSI pre-screening. Here, the potential predictive role of the most relevant artificial intelligence-driven models in predicting microsatellite instability directly from histology alone is discussed, focusing on CRC. The role of deep learning (DL) models in identifying the MSI status is here analyzed in the most relevant studies reporting the development of algorithms trained to this end. The most important performance and the most relevant deficiencies are discussed for every AI method. The models proposed for algorithm sharing among multiple research and clinical centers, including federal learning (FL) and swarm learning (SL), are reported. According to all the studies reported here, AI models are valuable tools for predicting MSI status on WSI alone in CRC. The use of digitized H&E-stained sections and a trained algorithm allow the extraction of relevant molecular information, such as MSI status, in a short time and at a low cost. The possible advantages related to introducing DL methods in routine surgical pathology are underlined here, and the acceleration of the digital transformation of pathology departments and services is recommended.
Collapse
Affiliation(s)
- Gavino Faa
- Dipartimento di Scienze Mediche e Sanità Pubblica, University of Cagliari, 09123 Cagliari, Italy;
| | - Ferdinando Coghe
- UOC Laboratorio Analisi, AOU of Cagliari, 09123 Cagliari, Italy;
| | - Andrea Pretta
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (A.P.); (M.S.)
| | - Massimo Castagnola
- Laboratorio di Proteomica, Centro Europeo di Ricerca sul Cervello, IRCCS Fondazione Santa Lucia, 00179 Rome, Italy;
| | - Peter Van Eyken
- Division of Pathology, Genk Regional Hospital, 3600 Genk, Belgium;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, University of Cagliari, 40138 Cagliari, Italy;
| | - Mario Scartozzi
- Medical Oncology Unit, University Hospital and University of Cagliari, 09042 Cagliari, Italy; (A.P.); (M.S.)
| | - Matteo Fraschini
- Dipartimento di Ingegneria Elettrica ed Elettronica, University of Cagliari, 09123 Cagliari, Italy
| |
Collapse
|
6
|
Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
Collapse
Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
| |
Collapse
|
7
|
Hirsch P, Molano LA, Engel A, Zentgraf J, Rahmann S, Hannig M, Müller R, Kern F, Keller A, Schmartz G. Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics. Nucleic Acids Res 2024; 52:W407-W414. [PMID: 38716863 PMCID: PMC11223814 DOI: 10.1093/nar/gkae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/03/2024] [Accepted: 04/24/2024] [Indexed: 07/06/2024] Open
Abstract
Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.
Collapse
Affiliation(s)
- Pascal Hirsch
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | | | - Annika Engel
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Jens Zentgraf
- Algorithmic Bioinformatics, Center for Bioinformatics Saar and Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
- Saarbrücken Graduate School of Computer Science, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Sven Rahmann
- Algorithmic Bioinformatics, Center for Bioinformatics Saar and Saarland University, Saarland Informatics Campus, 66123 Saarbrücken, Germany
| | - Matthias Hannig
- Clinic of Operative Dentistry, Periodontology and Preventive Dentistry, Saarland University Hospital, Saarland University, Kirrberger Str. 100, Building 73, 66421 Homburg, Saar, Germany
| | - Rolf Müller
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
- Deutsches Zentrum für Infektionsforschung (DZIF), Standort Hannover-Braunschweig, 38124 Braunschweig, Germany
- PharmaScienceHub, 66123 Saarbrücken, Germany
| | - Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research, 66123 Saarbrücken, Germany
- PharmaScienceHub, 66123 Saarbrücken, Germany
| | - Georges P Schmartz
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| |
Collapse
|
8
|
Wahl B, Butin G, Gombe S, Demiray A, Schwalbe N. Beyond "business as usual": lessons from FIFA for fair benefit-sharing in global health. HEALTH AFFAIRS SCHOLAR 2024; 2:qxae068. [PMID: 39050554 PMCID: PMC11267394 DOI: 10.1093/haschl/qxae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/06/2024] [Accepted: 05/15/2024] [Indexed: 07/27/2024]
Abstract
While researchers and agencies from low- and middle-income countries often contribute significantly to public health surveillance data, which is crucial for effective pandemic prevention, preparedness, and response activities, they often do not receive adequate compensation for their contributions. Incentivizing data sharing is important for informing public health responses to pathogens with pandemic potential. However, existing data-sharing legal frameworks have limitations. In this context, we looked beyond "business as usual" candidates to explore the applicability of a benefit-sharing model developed and implemented by the Fédération Internationale de Football Association (International Federation of Association Football; FIFA) in international association football. This model rewards grassroots contributions and redistributes benefits, promoting a fair balance of interests across diverse economic contexts. We discuss adapting FIFA's mechanisms, including training compensation and solidarity payments, to create a novel benefit-sharing framework in global health. Given the complexity of global health, we note ways in which components of the FIFA model would need to be adapted for global health. Challenges such as integrating into existing legal frameworks, ensuring broad international buy-in, and accommodating different pandemic periods are examined. While adapting the FIFA model presents challenges, it offers a promising approach to achieving more equitable data sharing and benefit distribution in global health.
Collapse
Affiliation(s)
- Brian Wahl
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States
- Spark Street Advisors, New York, NY 10013, United States
| | | | | | - Atalay Demiray
- Spark Street Advisors, New York, NY 10013, United States
- Department of Health Policy and Management, Yale School of Public Health, 350 George Street, New Haven, CT 06511, USA
| | - Nina Schwalbe
- Spark Street Advisors, New York, NY 10013, United States
- Heilbrunn Department of Population and Family Health, Columbia University, New York, NY 10032, United States
- United Nations University International Institute for Global Health, 56000 Kuala Lumpur, Malaysia
| |
Collapse
|
9
|
Lee CI, Tzeng CR, Li M, Lai HH, Chen CH, Huang Y, Chang TA, Chen CH, Huang CC, Lee MS, Liu M. Leveraging federated learning for boosting data privacy and performance in IVF embryo selection. J Assist Reprod Genet 2024; 41:1811-1820. [PMID: 38834757 PMCID: PMC11263320 DOI: 10.1007/s10815-024-03148-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 05/18/2024] [Indexed: 06/06/2024] Open
Abstract
PURPOSE To study the effectiveness of federated learning in in vitro fertilization on embryo evaluation tasks. METHODS This is a retrospective cohort analysis. Two datasets were used in this study. The ploidy status dataset consisted of 10,065 embryo records, 3760 treatments, and 2479 infertile couples from 5 hospitals. The clinical pregnancy dataset consisted of 4495 embryo records, 4495 treatments, and 3704 infertile couples from 4 hospitals. Federated learning and the gradient boosting decision tree algorithm were utilized for modeling. RESULTS On the ploidy status dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 71.78%, 73.10%, 69.39%, 69.72%, and 73.46% for 5 hospitals respectively, showing an average increase of 2.5% compared to those of our model trained without federated learning. On the clinical pregnancy dataset, the areas under the receiver operating characteristic curves of our model trained with federated learning were 72.03%, 56.77%, 61.63%, and 58.58% for 4 hospitals respectively, showing an average increase of 3.08%. CONCLUSIONS Federated learning can improve data privacy and data security and meanwhile improve the performance of embryo selection tasks by leveraging data from multiple sources. This study demonstrates the effectiveness of federated learning in embryo evaluation, and the results show the promise for future application.
Collapse
Affiliation(s)
- Chun-I Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | | | - Monty Li
- Becoming Reproductive Center, Taipei, Taiwan
| | - Hsing-Hua Lai
- Stork Fertility Center, Stork Ladies Clinic, Hsinchu, Taiwan
| | - Chi-Huang Chen
- Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yulun Huang
- Binflux, Inc, 4F.-1, No. 9, Dehui St., Zhongshan Dist, Taipei, 10461, Taiwan
| | - T Arthur Chang
- Department of Obstetrics and Gynecology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Chien-Hong Chen
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Chun-Chia Huang
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Maw-Sheng Lee
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Department of Obstetrics and Gynecology, Chung Shan Medical University, Taichung, Taiwan
- Division of Infertility, Lee Women's Hospital, Taichung, Taiwan
| | - Mark Liu
- Binflux, Inc, 4F.-1, No. 9, Dehui St., Zhongshan Dist, Taipei, 10461, Taiwan.
| |
Collapse
|
10
|
Carraro C, Montgomery JV, Klimmt J, Paquet D, Schultze JL, Beyer MD. Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs. Front Mol Neurosci 2024; 17:1414886. [PMID: 38952421 PMCID: PMC11215216 DOI: 10.3389/fnmol.2024.1414886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024] Open
Abstract
Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.
Collapse
Affiliation(s)
- Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| | - Jessica V. Montgomery
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| | - Julien Klimmt
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Dominik Paquet
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
| | - Marc D. Beyer
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
- PRECISE, Platform for Single Cell Genomics and Epigenomics at the German Center for Neurodegenerative Diseases and the University of Bonn and West German Genome Center, Bonn, Germany
- Immunogenomics & Neurodegeneration, Deutsches Zentrum für Neurodegenerative Erkrankungen e.V. (DZNE), Bonn, Germany
| |
Collapse
|
11
|
Shi Y, Zhou M, Chang C, Jiang P, Wei K, Zhao J, Shan Y, Zheng Y, Zhao F, Lv X, Guo S, Wang F, He D. Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management. Front Immunol 2024; 15:1409555. [PMID: 38915408 PMCID: PMC11194317 DOI: 10.3389/fimmu.2024.1409555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 05/24/2024] [Indexed: 06/26/2024] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.
Collapse
Affiliation(s)
- Yiming Shi
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Mi Zhou
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Jianan Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yu Shan
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Yixin Zheng
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Fuyu Zhao
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Xinliang Lv
- Traditional Chinese Medicine Hospital of Inner Mongolia Autonomous Region, Hohhot, Inner Mongolia Autonomous Region, China
| | - Shicheng Guo
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Fubo Wang
- Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, China
- Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China
| | - Dongyi He
- Department of Rheumatology, Shanghai Guanghua Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|
12
|
Liu Y, Huang J, Chen JC, Chen W, Pan Y, Qiu J. Predicting treatment response in multicenter non-small cell lung cancer patients based on federated learning. BMC Cancer 2024; 24:688. [PMID: 38840081 PMCID: PMC11155008 DOI: 10.1186/s12885-024-12456-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 05/30/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Multicenter non-small cell lung cancer (NSCLC) patient data is information-rich. However, its direct integration becomes exceptionally challenging due to constraints involving different healthcare organizations and regulations. Traditional centralized machine learning methods require centralizing these sensitive medical data for training, posing risks of patient privacy leakage and data security issues. In this context, federated learning (FL) has attracted much attention as a distributed machine learning framework. It effectively addresses this contradiction by preserving data locally, conducting local model training, and aggregating model parameters. This approach enables the utilization of multicenter data with maximum benefit while ensuring privacy safeguards. Based on pre-radiotherapy planning target volume images of NSCLC patients, a multicenter treatment response prediction model is designed by FL for predicting the probability of remission of NSCLC patients. This approach ensures medical data privacy, high prediction accuracy and computing efficiency, offering valuable insights for clinical decision-making. METHODS We retrospectively collected CT images from 245 NSCLC patients undergoing chemotherapy and radiotherapy (CRT) in four Chinese hospitals. In a simulation environment, we compared the performance of the centralized deep learning (DL) model with that of the FL model using data from two sites. Additionally, due to the unavailability of data from one hospital, we established a real-world FL model using data from three sites. Assessments were conducted using measures such as accuracy, receiver operating characteristic curve, and confusion matrices. RESULTS The model's prediction performance obtained using FL methods outperforms that of traditional centralized learning methods. In the comparative experiment, the DL model achieves an AUC of 0.718/0.695, while the FL model demonstrates an AUC of 0.725/0.689, with real-world FL model achieving an AUC of 0.698/0.672. CONCLUSIONS We demonstrate that the performance of a FL predictive model, developed by combining convolutional neural networks (CNNs) with data from multiple medical centers, is comparable to that of a traditional DL model obtained through centralized training. It can efficiently predict CRT treatment response in NSCLC patients while preserving privacy.
Collapse
Affiliation(s)
- Yuan Liu
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Jinzao Huang
- Department of Radiology, Cathay General Hospital, Taipei, China
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao- Tung University, Taipei, China
| | - Jyh-Cheng Chen
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao- Tung University, Taipei, China
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, China
| | - Wei Chen
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Yuteng Pan
- School of Radiology, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China
| | - Jianfeng Qiu
- School of Radiology, Second Affiliated Hospital of Shandong First Medical University and Shandong Academy of Medical Sciences, Taian, China.
| |
Collapse
|
13
|
Gim N, Wu Y, Blazes M, Lee CS, Wang RK, Lee AY. A Clinician's Guide to Sharing Data for AI in Ophthalmology. Invest Ophthalmol Vis Sci 2024; 65:21. [PMID: 38864811 PMCID: PMC11174091 DOI: 10.1167/iovs.65.6.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 05/17/2024] [Indexed: 06/13/2024] Open
Abstract
Data is the cornerstone of using AI models, because their performance directly depends on the diversity, quantity, and quality of the data used for training. Using AI presents unique potential, particularly in medical applications that involve rich data such as ophthalmology, encompassing a variety of imaging methods, medical records, and eye-tracking data. However, sharing medical data comes with challenges because of regulatory issues and privacy concerns. This review explores traditional and nontraditional data sharing methods in medicine, focusing on previous works in ophthalmology. Traditional methods involve direct data transfer, whereas newer approaches prioritize security and privacy by sharing derived datasets, creating secure research environments, or using model-to-data strategies. We examine each method's mechanisms, variations, recent applications in ophthalmology, and their respective advantages and disadvantages. By empowering medical researchers with insights into data sharing methods and considerations, this review aims to assist informed decision-making while upholding ethical standards and patient privacy in medical AI development.
Collapse
Affiliation(s)
- Nayoon Gim
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- The Roger and Angie Karalis Retina Center, Seattle, Washington, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- The Roger and Angie Karalis Retina Center, Seattle, Washington, United States
| | - Marian Blazes
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- The Roger and Angie Karalis Retina Center, Seattle, Washington, United States
| | - Cecilia S. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- The Roger and Angie Karalis Retina Center, Seattle, Washington, United States
| | - Ruikang K. Wang
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, United States
- The Roger and Angie Karalis Retina Center, Seattle, Washington, United States
| |
Collapse
|
14
|
Hwang H, Jeon H, Yeo N, Baek D. Big data and deep learning for RNA biology. Exp Mol Med 2024; 56:1293-1321. [PMID: 38871816 PMCID: PMC11263376 DOI: 10.1038/s12276-024-01243-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
Collapse
Affiliation(s)
- Hyeonseo Hwang
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyeonseong Jeon
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
- Genome4me Inc., Seoul, Republic of Korea
| | - Nagyeong Yeo
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Daehyun Baek
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea.
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Genome4me Inc., Seoul, Republic of Korea.
| |
Collapse
|
15
|
Hu M, Shi X, Song PXK. Collaborative inference for treatment effect with distributed data-sharing management in multicenter studies. Stat Med 2024; 43:2263-2279. [PMID: 38551130 DOI: 10.1002/sim.10068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/01/2024] [Accepted: 03/14/2024] [Indexed: 05/18/2024]
Abstract
Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time-consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta-analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post-transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject-level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large-sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney-transplant patients compared to the standard-of-care.
Collapse
Affiliation(s)
- Mengtong Hu
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| | - Peter X-K Song
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan
| |
Collapse
|
16
|
Coutinho-Almeida J, Cruz-Correia RJ, Rodrigues PP. Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models. Sci Rep 2024; 14:11128. [PMID: 38750112 PMCID: PMC11096161 DOI: 10.1038/s41598-024-61371-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 05/06/2024] [Indexed: 05/18/2024] Open
Abstract
This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.
Collapse
Affiliation(s)
- João Coutinho-Almeida
- CINTESIS@RISE-Centre for Health Technologies and Services Research, University of Porto, Porto, Portugal.
- Health Data Science PhD Program, Faculty of Medicine, University of Porto, Porto, Portugal.
| | - Ricardo João Cruz-Correia
- CINTESIS@RISE-Centre for Health Technologies and Services Research, University of Porto, Porto, Portugal
- MEDCIDS-Faculty of Medicine, University of Porto, Porto, Portugal
- Health Data Science PhD Program, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Pedro Pereira Rodrigues
- CINTESIS@RISE-Centre for Health Technologies and Services Research, University of Porto, Porto, Portugal
- MEDCIDS-Faculty of Medicine, University of Porto, Porto, Portugal
- Health Data Science PhD Program, Faculty of Medicine, University of Porto, Porto, Portugal
| |
Collapse
|
17
|
Chakshu NK, Nithiarasu P. Orbital learning: a novel, actively orchestrated decentralised learning for healthcare. Sci Rep 2024; 14:10459. [PMID: 38714825 PMCID: PMC11076556 DOI: 10.1038/s41598-024-60915-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 04/29/2024] [Indexed: 05/10/2024] Open
Abstract
A novel collaborative and continual learning across a network of decentralised healthcare units, avoiding identifiable data-sharing capacity, is proposed. Currently available methodologies, such as federated learning and swarm learning, have demonstrated decentralised learning. However, the majority of them face shortcomings that affect their performance and accuracy. These shortcomings include a non-uniform rate of data accumulation, non-uniform patient demographics, biased human labelling, and erroneous or malicious training data. A novel method to reduce such shortcomings is proposed in the present work through selective grouping and displacing of actors in a network of many entities for intra-group sharing of learning with inter-group accessibility. The proposed system, known as Orbital Learning, incorporates various features from split learning and ensemble learning for a robust and secure performance of supervised models. A digital embodiment of the information quality and flow within a decentralised network, this platform also acts as a digital twin of healthcare network. An example of ECG classification for arrhythmia with 6 clients is used to analyse its performance and is compared against federated learning. In this example, four separate experiments are conducted with varied configurations, such as varied age demographics and clients with data tampering. The results obtained show an average area under receiver operating characteristic curve (AUROC) of 0.819 (95% CI 0.784-0.853) for orbital learning whereas 0.714 (95% CI 0.692-0.736) for federated learning. This result shows an increase in overall performance and establishes that the proposed system can address the majority of the issues faced by existing decentralised learning methodologies. Further, a scalability demo conducted establishes the versatility and scalability of this platform in handling state-of-the-art large language models.
Collapse
Affiliation(s)
- Neeraj Kavan Chakshu
- Zienkiewicz Institute for Modelling, Data and AI, Bay Campus, Fabian Way, Crymlyn Burrows, Swansea University, Swansea, Wales, SA1 8EN, UK
| | - Perumal Nithiarasu
- Zienkiewicz Institute for Modelling, Data and AI, Bay Campus, Fabian Way, Crymlyn Burrows, Swansea University, Swansea, Wales, SA1 8EN, UK.
| |
Collapse
|
18
|
Zhou J, Huang C, Gao X. Patient privacy in AI-driven omics methods. Trends Genet 2024; 40:383-386. [PMID: 38637270 DOI: 10.1016/j.tig.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024]
Abstract
Artificial intelligence (AI) in omics analysis raises privacy threats to patients. Here, we briefly discuss risk factors to patient privacy in data sharing, model training, and release, as well as methods to safeguard and evaluate patient privacy in AI-driven omics methods.
Collapse
Affiliation(s)
- Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Chao Huang
- Ningbo Institute of Information Technology Application, Chinese Academy of Sciences (CAS), Ningbo, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
| |
Collapse
|
19
|
Chen J, Pan R. Medical report generation based on multimodal federated learning. Comput Med Imaging Graph 2024; 113:102342. [PMID: 38309174 DOI: 10.1016/j.compmedimag.2024.102342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/20/2024] [Accepted: 01/20/2024] [Indexed: 02/05/2024]
Abstract
Medical image reports are integral to clinical decision-making and patient management. Despite their importance, the confidentiality and private nature of medical data pose significant issues for the sharing and analysis of medical image data. This paper addresses these concerns by introducing a multimodal federated learning-based methodology for medical image reporting. This methodology harnesses distributed computing for co-training models across various medical institutions. Under the federated learning framework, every medical institution is capable of training the model locally and aggregating the updated model parameters to curate a top-tier medical image report model. Initially, we advocate for an architecture facilitating multimodal federated learning, including model creation, parameter consolidation, and algorithm enhancement steps. In the model selection phase, we introduce a deep learning-based strategy that utilizes multimodal data for training to produce medical image reports. In the parameter aggregation phase, the federal average algorithm is applied to amalgamate model parameters trained by each institution, which leads to a comprehensive global model. In addition, we introduce an evidence-based optimization algorithm built upon the federal average algorithm. The efficacy of the proposed architecture and scheme is showcased through a series of experiments. Our experimental results validate the proficiency of the proposed multimodal federated learning approach in generating medical image reports. Compared to conventional centralized learning methods, our proposal not only enhances the protection of patient confidentiality but also enriches the accuracy and overall quality of medical image reports. Through this research, we offer a novel solution for the privacy issues linked with the sharing and analyzing of medical data. Expected to assume a crucial role in medical image report generation and other medical applications, the multimodal federated learning method is set to deliver more precise, efficient, and privacy-secured medical services for healthcare professionals and patients.
Collapse
Affiliation(s)
- Jieying Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
| | - Rong Pan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
20
|
Unger M, Kather JN. Deep learning in cancer genomics and histopathology. Genome Med 2024; 16:44. [PMID: 38539231 PMCID: PMC10976780 DOI: 10.1186/s13073-024-01315-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/13/2024] [Indexed: 07/08/2024] Open
Abstract
Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. In this review, we summarize current and emerging applications of DL in histopathology and genomics, including basic diagnostic as well as advanced prognostic tasks. Based on a growing body of evidence, we suggest that DL could be the groundwork for a new kind of workflow in oncology and cancer research. However, we also point out that DL models can have biases and other flaws that users in healthcare and research need to know about, and we propose ways to address them.
Collapse
Affiliation(s)
- Michaela Unger
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
- Department of Medicine I, University Hospital Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
| |
Collapse
|
21
|
Eckardt JN, Hahn W, Röllig C, Stasik S, Platzbecker U, Müller-Tidow C, Serve H, Baldus CD, Schliemann C, Schäfer-Eckart K, Hanoun M, Kaufmann M, Burchert A, Thiede C, Schetelig J, Sedlmayr M, Bornhäuser M, Wolfien M, Middeke JM. Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence. NPJ Digit Med 2024; 7:76. [PMID: 38509224 PMCID: PMC10954666 DOI: 10.1038/s41746-024-01076-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 03/07/2024] [Indexed: 03/22/2024] Open
Abstract
Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence - CTAB-GAN+ and normalizing flows (NFlow) - to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.
Collapse
Affiliation(s)
- Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Else Kröner Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
| | - Waldemar Hahn
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Leipzig, Germany
- Institute for Medical Informatics and Biometry, Technical University Dresden, Dresden, Germany
| | - Christoph Röllig
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Sebastian Stasik
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Uwe Platzbecker
- Medical Clinic and Policlinic I Hematology and Cell Therapy, University Hospital, Leipzig, Germany
| | | | - Hubert Serve
- Department of Medicine 2, Hematology and Oncology, Goethe University Frankfurt, Frankfurt, Germany
| | - Claudia D Baldus
- Department of Hematology and Oncology, University Hospital Schleswig Holstein, Kiel, Germany
| | | | - Kerstin Schäfer-Eckart
- Department of Internal Medicine V, Paracelsus Medizinische Privatuniversität and University Hospital Nürnberg, Nürnberg, Germany
| | - Maher Hanoun
- Department of Hematology, University Hospital Essen, Essen, Germany
| | - Martin Kaufmann
- Department of Hematology, Oncology and Palliative Care, Robert-Bosch-Hospital, Stuttgart, Germany
| | - Andreas Burchert
- Department of Hematology, Oncology and Immunology, Philipps-University-Marburg, Marburg, Germany
| | - Christian Thiede
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Johannes Schetelig
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Martin Sedlmayr
- Institute for Medical Informatics and Biometry, Technical University Dresden, Dresden, Germany
| | - Martin Bornhäuser
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- German Consortium for Translational Cancer Research DKFZ, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany
| | - Markus Wolfien
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Leipzig, Germany
- Institute for Medical Informatics and Biometry, Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kröner Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| |
Collapse
|
22
|
Haggenmüller S, Schmitt M, Krieghoff-Henning E, Hekler A, Maron RC, Wies C, Utikal JS, Meier F, Hobelsberger S, Gellrich FF, Sergon M, Hauschild A, French LE, Heinzerling L, Schlager JG, Ghoreschi K, Schlaak M, Hilke FJ, Poch G, Korsing S, Berking C, Heppt MV, Erdmann M, Haferkamp S, Drexler K, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather JN, Fröhling S, Brinker TJ. Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics. JAMA Dermatol 2024; 160:303-311. [PMID: 38324293 PMCID: PMC10851139 DOI: 10.1001/jamadermatol.2023.5550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/01/2023] [Indexed: 02/08/2024]
Abstract
Importance The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals. Objective To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics. Design, Setting, and Participants This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological whole-slide images prospectively acquired at 6 German university hospitals between April 2021 and February 2023 and benchmarked it using both a holdout and an external test dataset. Data analysis was performed from February to April 2023. Exposures All whole-slide images were retrospectively analyzed by an AI-based classifier without influencing routine clinical care. Main Outcomes and Measures The area under the receiver operating characteristic curve (AUROC) served as the primary end point for evaluating the diagnostic performance. Secondary end points included balanced accuracy, sensitivity, and specificity. Results The study included 1025 whole-slide images of clinically melanoma-suspicious skin lesions from 923 patients, consisting of 388 histopathologically confirmed invasive melanomas and 637 nevi. The median (range) age at diagnosis was 58 (18-95) years for the training set, 57 (18-93) years for the holdout test dataset, and 61 (18-95) years for the external test dataset; the median (range) Breslow thickness was 0.70 (0.10-34.00) mm, 0.70 (0.20-14.40) mm, and 0.80 (0.30-20.00) mm, respectively. The federated approach (0.8579; 95% CI, 0.7693-0.9299) performed significantly worse than the classical centralized approach (0.9024; 95% CI, 0.8379-0.9565) in terms of AUROC on a holdout test dataset (pairwise Wilcoxon signed-rank, P < .001) but performed significantly better (0.9126; 95% CI, 0.8810-0.9412) than the classical centralized approach (0.9045; 95% CI, 0.8701-0.9331) on an external test dataset (pairwise Wilcoxon signed-rank, P < .001). Notably, the federated approach performed significantly worse than the ensemble approach on both the holdout (0.8867; 95% CI, 0.8103-0.9481) and external test dataset (0.9227; 95% CI, 0.8941-0.9479). Conclusions and Relevance The findings of this diagnostic study suggest that federated learning is a viable approach for the binary classification of invasive melanomas and nevi on a clinically representative distributed dataset. Federated learning can improve privacy protection in AI-based melanoma diagnostics while simultaneously promoting collaboration across institutions and countries. Moreover, it may have the potential to be extended to other image classification tasks in digital cancer histopathology and beyond.
Collapse
Affiliation(s)
- Sarah Haggenmüller
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Max Schmitt
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Achim Hekler
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Roman C. Maron
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christoph Wies
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jochen S. Utikal
- Department of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karls University of Heidelberg, Mannheim, Germany
- Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany
- DKFZ Hector Cancer Institute at the University Medical Center Mannheim, Mannheim, Germany
| | - Friedegund Meier
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Sarah Hobelsberger
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Frank F. Gellrich
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Mildred Sergon
- Skin Cancer Center at the University Cancer Center and National Center for Tumor Diseases Dresden, Department of Dermatology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Axel Hauschild
- Department of Dermatology, University Hospital (UKSH), Kiel, Germany
| | - Lars E. French
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
- Dr Phillip Frost Department of Dermatology and Cutaneous Surgery, Miller School of Medicine, University of Miami, Miami, Florida
| | - Lucie Heinzerling
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Justin G. Schlager
- Department of Dermatology and Allergy, University Hospital, LMU Munich, Munich, Germany
| | - Kamran Ghoreschi
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Max Schlaak
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Franz J. Hilke
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gabriela Poch
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sören Korsing
- Department of Dermatology, Venereology and Allergology, Charité–Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Carola Berking
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Markus V. Heppt
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, University Hospital Erlangen, Comprehensive Cancer Center Erlangen–European Metropolitan Region Nürnberg, CCC Alliance WERA, Erlangen, Germany
| | - Sebastian Haferkamp
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Konstantin Drexler
- Department of Dermatology, University Hospital Regensburg, Regensburg, Germany
| | - Dirk Schadendorf
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Wiebke Sondermann
- Department of Dermatology, Venereology and Allergology, University Hospital Essen, Essen, Germany
| | - Matthias Goebeler
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg and National Center for Tumor Diseases (NCT) WERA, Würzburg, Germany
| | - Bastian Schilling
- Department of Dermatology, Venereology and Allergology, University Hospital Würzburg and National Center for Tumor Diseases (NCT) WERA, Würzburg, Germany
| | - Jakob N. Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J. Brinker
- Digital Biomarkers for Oncology Group, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|
23
|
Fraioli F, Albert N, Boellaard R, Galazzo IB, Brendel M, Buvat I, Castellaro M, Cecchin D, Fernandez PA, Guedj E, Hammers A, Kaplar Z, Morbelli S, Papp L, Shi K, Tolboom N, Traub-Weidinger T, Verger A, Van Weehaeghe D, Yakushev I, Barthel H. Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging. Eur J Nucl Med Mol Imaging 2024; 51:1007-1011. [PMID: 38097746 DOI: 10.1007/s00259-023-06553-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Affiliation(s)
- Francesco Fraioli
- Institute of Nuclear Medicine, University College London Hospitals, 5Th Floor UCH, 235 Euston Rd, London, NW1 2BU, UK.
| | - Nathalie Albert
- Department of Nuclear Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Ronald Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | | | - Matthias Brendel
- Department of Nuclear Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Irene Buvat
- Institut Curie - Inserm Laboratory of Translational Imaging in Oncology, Paris, France
| | - Marco Castellaro
- Department of Information Engineering, University-Hospital of Padova, Padua, Italy
| | - Diego Cecchin
- Nuclear Medicine Unit, Department of Medicine - DIMED, University-Hospital of Padova, Padua, Italy
| | - Pablo Aguiar Fernandez
- CIMUS, Universidade Santiago de Compostela & Nuclear Medicine Dept, Univ. Hospital IDIS, Santiago de Compostela, Spain
| | - Eric Guedj
- Département de Médecine Nucléaire, Aix Marseille Univ, APHM, CNRS, Centrale Marseille, Institut Fresnel, Hôpital de La Timone, CERIMED, Marseille, France
| | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, King's College London St Thomas' Hospital, London, SE1 7EH, UK
| | - Zoltan Kaplar
- Institute of Nuclear Medicine, University College London Hospitals, 5Th Floor UCH, 235 Euston Rd, London, NW1 2BU, UK
| | - Silvia Morbelli
- Nuclear Medicine Unit, AOU Città Della Salute E Della Scienza Di Torino, University of Turin, Turin, Italy
| | - Laszlo Papp
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Kuangyu Shi
- Lab for Artificial Intelligence and Translational Theranostic, Dept. of Nuclear Medicine, University of Bern, Bern, Switzerland
| | - Nelleke Tolboom
- Department of Radiology and Nuclear Medicine, Utrecht University Medical Center, Utrecht, The Netherlands
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy, Université de Lorraine, IADI, INSERM U1254, Nancy, France
| | - Donatienne Van Weehaeghe
- Department of Radiology and Nuclear Medicine, Ghent University Hospital, C. Heymanslaan 10, 9000, Ghent, Belgium
| | - Igor Yakushev
- Department of Nuclear Medicine, School of Medicine, Technical University of Munich, Munich, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Medical Centre, Leipzig, Germany
| |
Collapse
|
24
|
Golubnitschaja O, Polivka J, Potuznik P, Pesta M, Stetkarova I, Mazurakova A, Lackova L, Kubatka P, Kropp M, Thumann G, Erb C, Fröhlich H, Wang W, Baban B, Kapalla M, Shapira N, Richter K, Karabatsiakis A, Smokovski I, Schmeel LC, Gkika E, Paul F, Parini P, Polivka J. The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation. EPMA J 2024; 15:1-23. [PMID: 38463624 PMCID: PMC10923756 DOI: 10.1007/s13167-024-00356-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.
Collapse
Affiliation(s)
- Olga Golubnitschaja
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | - Jiri Polivka
- Department of Histology and Embryology, Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
- Biomedical Centre, Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| | - Pavel Potuznik
- Department of Neurology, University Hospital Plzen and Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| | - Martin Pesta
- Department of Biology, Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| | - Ivana Stetkarova
- Department of Neurology, University Hospital Kralovske Vinohrady, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Alena Mazurakova
- Department of Anatomy, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Lenka Lackova
- Department of Histology and Embryology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Peter Kubatka
- Department of Histology and Embryology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Martina Kropp
- Experimental Ophthalmology, University of Geneva, 1205 Geneva, Switzerland
- Ophthalmology Department, University Hospitals of Geneva, 1205 Geneva, Switzerland
| | - Gabriele Thumann
- Experimental Ophthalmology, University of Geneva, 1205 Geneva, Switzerland
- Ophthalmology Department, University Hospitals of Geneva, 1205 Geneva, Switzerland
| | - Carl Erb
- Private Institute of Applied Ophthalmology, Berlin, Germany
| | - Holger Fröhlich
- Artificial Intelligence & Data Science Group, Fraunhofer SCAI, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (B-It), University of Bonn, 53115 Bonn, Germany
| | - Wei Wang
- Edith Cowan University, Perth, Australia
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Babak Baban
- The Dental College of Georgia, Departments of Neurology and Surgery, The Medical College of Georgia, Augusta University, Augusta, USA
| | - Marko Kapalla
- Negentropic Systems, Ružomberok, Slovakia
- PPPM Centre, s.r.o., Ruzomberok, Slovakia
| | - Niva Shapira
- Department of Nutrition, School of Health Sciences, Ashkelon Academic College, Ashkelon, Israel
| | - Kneginja Richter
- CuraMed Tagesklinik Nürnberg GmbH, Nuremberg, Germany
- Technische Hochschule Nürnberg GSO, Nuremberg, Germany
- University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany
| | - Alexander Karabatsiakis
- Department of Psychology, Clinical Psychology II, University of Innsbruck, Innsbruck, Austria
| | - Ivica Smokovski
- University Clinic of Endocrinology, Diabetes and Metabolic Disorders Skopje, University Goce Delcev, Faculty of Medical Sciences, Stip, North Macedonia
| | - Leonard Christopher Schmeel
- Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | - Eleni Gkika
- Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | | | - Paolo Parini
- Cardio Metabolic Unit, Department of Medicine Huddinge, and Department of Laboratory Medicine, Karolinska Institutet, and Medicine Unit of Endocrinology, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden
| | - Jiri Polivka
- Department of Neurology, University Hospital Plzen and Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| |
Collapse
|
25
|
Fang C, Dziedzic A, Zhang L, Oliva L, Verma A, Razak F, Papernot N, Wang B. Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data. EBioMedicine 2024; 101:105006. [PMID: 38377795 PMCID: PMC10884342 DOI: 10.1016/j.ebiom.2024.105006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. METHODS In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization); (2) it safeguards patients' privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized party/server. FINDINGS We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private datasets from individual parties without collaboration and those trained with the previous privacy-preserving collaborative training framework under the same privacy guarantee by up to 70% and 18.2% respectively. INTERPRETATION We demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing DeCaPH enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability. FUNDING This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294), Canadian Institute for Advanced Research (CIFAR) AI Catalyst Grants, CIFAR AI Chair programs, Temerty Professor of AI Research and Education in Medicine, University of Toronto, Amazon, Apple, DARPA through the GARD project, Intel, Meta, the Ontario Early Researcher Award, and the Sloan Foundation. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.
Collapse
Affiliation(s)
- Congyu Fang
- Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada
| | - Adam Dziedzic
- Vector Institute, Toronto, Canada; CISPA Helmholtz Center for Information Security, Germany; Department of Electrical and Computer Engineering, University of Toronto, Canada
| | - Lin Zhang
- Peter Munk Cardiac Centre, University Health Network, Canada; Simon Fraser University, Canada
| | - Laura Oliva
- Peter Munk Cardiac Centre, University Health Network, Canada
| | - Amol Verma
- St. Michael's Hospital, Unity Health Toronto, Canada; Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada
| | - Fahad Razak
- St. Michael's Hospital, Unity Health Toronto, Canada; Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada
| | - Nicolas Papernot
- Department of Computer Science, University of Toronto, Canada; Vector Institute, Toronto, Canada; Department of Electrical and Computer Engineering, University of Toronto, Canada.
| | - Bo Wang
- Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Canada.
| |
Collapse
|
26
|
Kawamura Y, Vafaei Sadr A, Abedi V, Zand R. Many Models, Little Adoption-What Accounts for Low Uptake of Machine Learning Models for Atrial Fibrillation Prediction and Detection? J Clin Med 2024; 13:1313. [PMID: 38592138 PMCID: PMC10932407 DOI: 10.3390/jcm13051313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 04/10/2024] Open
Abstract
(1) Background: Atrial fibrillation (AF) is a major risk factor for stroke and is often underdiagnosed, despite being present in 13-26% of ischemic stroke patients. Recently, a significant number of machine learning (ML)-based models have been proposed for AF prediction and detection for primary and secondary stroke prevention. However, clinical translation of these technological innovations to close the AF care gap has been scant. Herein, we sought to systematically examine studies, employing ML models to predict incident AF in a population without prior AF or to detect paroxysmal AF in stroke cohorts to identify key reasons for the lack of translation into the clinical workflow. We conclude with a set of recommendations to improve the clinical translatability of ML-based models for AF. (2) Methods: MEDLINE, Embase, Web of Science, Clinicaltrials.gov, and ICTRP databases were searched for relevant articles from the inception of the databases up to September 2022 to identify peer-reviewed articles in English that used ML methods to predict incident AF or detect AF after stroke and reported adequate performance metrics. The search yielded 2815 articles, of which 16 studies using ML models to predict incident AF and three studies focusing on ML models to detect AF post-stroke were included. (3) Conclusions: This study highlights that (1) many models utilized only a limited subset of variables available from patients' health records; (2) only 37% of models were externally validated, and stratified analysis was often lacking; (3) 0% of models and 53% of datasets were explicitly made available, limiting reproducibility and transparency; and (4) data pre-processing did not include bias mitigation and sufficient details, leading to potential selection bias. Low generalizability, high false alarm rate, and lack of interpretability were identified as additional factors to be addressed before ML models can be widely deployed in the clinical care setting. Given these limitations, our recommendations to improve the uptake of ML models for better AF outcomes include improving generalizability, reducing potential systemic biases, and investing in external validation studies whilst developing a transparent modeling pipeline to ensure reproducibility.
Collapse
Affiliation(s)
- Yuki Kawamura
- School of Clinical Medicine, University of Cambridge, Cambridge CB3 0SP, UK
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Vida Abedi
- Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA (V.A.)
| | - Ramin Zand
- Department of Neurology, College of Medicine, The Pennsylvania State University, Hershey, PA 17033, USA
| |
Collapse
|
27
|
Xu X, Li J, Zhu Z, Zhao L, Wang H, Song C, Chen Y, Zhao Q, Yang J, Pei Y. A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis. Bioengineering (Basel) 2024; 11:219. [PMID: 38534493 DOI: 10.3390/bioengineering11030219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/15/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.
Collapse
Affiliation(s)
- Xi Xu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Zhichao Zhu
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Linna Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Huina Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Changwei Song
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yining Chen
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Qing Zhao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jijiang Yang
- Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
| | - Yan Pei
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
| |
Collapse
|
28
|
Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
Collapse
Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
| |
Collapse
|
29
|
Teo ZL, Jin L, Li S, Miao D, Zhang X, Ng WY, Tan TF, Lee DM, Chua KJ, Heng J, Liu Y, Goh RSM, Ting DSW. Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture. Cell Rep Med 2024; 5:101419. [PMID: 38340728 PMCID: PMC10897620 DOI: 10.1016/j.xcrm.2024.101419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 11/17/2023] [Accepted: 01/18/2024] [Indexed: 02/12/2024]
Abstract
Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection needs. By conducting a systematic review of FL applications in healthcare, we identify relevant articles in scientific, engineering, and medical journals in English up to August 31st, 2023. Out of a total of 22,693 articles under review, 612 articles are included in the final analysis. The majority of articles are proof-of-concepts studies, and only 5.2% are studies with real-life application of FL. Radiology and internal medicine are the most common specialties involved in FL. FL is robust to a variety of machine learning models and data types, with neural networks and medical imaging being the most common, respectively. We highlight the need to address the barriers to clinical translation and to assess its real-world impact in this new digital data-driven healthcare scene.
Collapse
Affiliation(s)
- Zhen Ling Teo
- Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore
| | - Liyuan Jin
- Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore
| | - Siqi Li
- Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore
| | - Di Miao
- Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore
| | - Xiaoman Zhang
- Singapore Eye Research Institute, Singapore, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore
| | - Ting Fang Tan
- Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore
| | - Deborah Meixuan Lee
- Singapore Eye Research Institute, Singapore, Singapore; Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore
| | - Kai Jie Chua
- Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore
| | - John Heng
- Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore
| | - Yong Liu
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Rick Siow Mong Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore, Singapore; Singapore Eye Research Institute, Singapore, Singapore; Duke-NUS Medical School, Singapore, Singapore.
| |
Collapse
|
30
|
Delfin C, Dragan I, Kuznetsov D, Tajes JF, Smit F, Coral DE, Farzaneh A, Haugg A, Hungele A, Niknejad A, Hall C, Jacobs D, Marek D, Fraser DP, Thuillier D, Ahmadizar F, Mehl F, Pattou F, Burdet F, Hawkes G, Arts ICW, Blanch J, Van Soest J, Fernández-Real JM, Boehl J, Fink K, van Greevenbroek MMJ, Kavousi M, Minten M, Prinz N, Ipsen N, Franks PW, Ramos R, Holl RW, Horban S, Duarte-Salles T, Tran VDT, Raverdy V, Leal Y, Lenart A, Pearson E, Sparsø T, Giordano GN, Ioannidis V, Soh K, Frayling TM, Le Roux CW, Ibberson M. A Federated Database for Obesity Research: An IMI-SOPHIA Study. Life (Basel) 2024; 14:262. [PMID: 38398771 PMCID: PMC10890572 DOI: 10.3390/life14020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/12/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.
Collapse
Affiliation(s)
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Juan Fernandez Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Femke Smit
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Daniel E. Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Ali Farzaneh
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - André Haugg
- Global Biostatistics & Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach, Germany
| | - Andreas Hungele
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Christopher Hall
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | - Daan Jacobs
- Nederlandse Obesitas Kliniek, Huis Ter Heide, 3712 BA Utrecht, The Netherlands
| | - Diana Marek
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Diane P. Fraser
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Dorothee Thuillier
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Fariba Ahmadizar
- Data Science and Biostatistics Department, Julius Global Health, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Francois Pattou
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Frederic Burdet
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Gareth Hawkes
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Ilja C. W. Arts
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Jordi Blanch
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- ISV-Girona Research Group, Research Unit in Primary Care, Primary Care Services, Catalan Institute of Health (ICS), 08908 Barcelona, Spain
| | - Johan Van Soest
- Brightlands Institute for Smart Society (BISS), Faculty of Science and Engineering, Maastricht University, 6229 EN Maastricht, The Netherlands
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Center, 6229 EN Maastricht, The Netherlands
| | - José-Manuel Fernández-Real
- Nutrition, Eumetabolism and Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Av. França 30, 17007 Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Av. França, s/n, 17007 Girona, Spain
| | - Juergen Boehl
- Global Biostatistics & Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach, Germany
| | - Katharina Fink
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Marleen M. J. van Greevenbroek
- Department of Internal Medicine and CARIM School of Cardiovascular Diseases, Maastricht University, 6229 EN Maastricht, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Michiel Minten
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Nicole Prinz
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | | | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Rafael Ramos
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
- Research in Vascular Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Parc Hospitalari Martí i Julià, Edifici M2, 17190 Salt, Spain
| | - Reinhard W. Holl
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Scott Horban
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Van Du T. Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Violeta Raverdy
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Yenny Leal
- Nutrition, Eumetabolism and Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Av. França 30, 17007 Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Av. França, s/n, 17007 Girona, Spain
| | | | - Ewan Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | | | - Giuseppe N. Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Vassilios Ioannidis
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Keng Soh
- Novo Nordisk A/S, 2860 Søborg, Denmark
| | - Timothy M. Frayling
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, 1 Rue Michel-Servet, CH-1211 Geneva, Switzerland
| | - Carel W. Le Roux
- Diabetes Complications Research Centre, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| |
Collapse
|
31
|
Rajendran S, Pan W, Sabuncu MR, Chen Y, Zhou J, Wang F. Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation. PATTERNS (NEW YORK, N.Y.) 2024; 5:100913. [PMID: 38370129 PMCID: PMC10873158 DOI: 10.1016/j.patter.2023.100913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C4) integration in such contexts: the process of combining information from diverse datasets distributed across distinct, secure sites. We argue that C4 approaches could pave the way for ML models that are both holistic and widely applicable. This paper provides a comprehensive overview of C4 in health care, including its present stage, potential opportunities, and associated challenges.
Collapse
Affiliation(s)
- Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Cornell University, Ithaca, NY, USA
| | - Weishen Pan
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Mert R. Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
- Cornell Tech, Cornell University, New York, NY, USA
- Department of Radiology, Weill Cornell Medical School, New York, NY, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiayu Zhou
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Fei Wang
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| |
Collapse
|
32
|
Zhou J, Chen S, Wu Y, Li H, Zhang B, Zhou L, Hu Y, Xiang Z, Li Z, Chen N, Han W, Xu C, Wang D, Gao X. PPML-Omics: A privacy-preserving federated machine learning method protects patients' privacy in omic data. SCIENCE ADVANCES 2024; 10:eadh8601. [PMID: 38295178 PMCID: PMC10830108 DOI: 10.1126/sciadv.adh8601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 12/29/2023] [Indexed: 02/02/2024]
Abstract
Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.
Collapse
Affiliation(s)
- Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Siyuan Chen
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Yulian Wu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Haoyang Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Bin Zhang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Longxi Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Yan Hu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Zihang Xiang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Zhongxiao Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ningning Chen
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Chencheng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Di Wang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
- Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia
| |
Collapse
|
33
|
Zhou J, Zhou L, Wang D, Xu X, Li H, Chu Y, Han W, Gao X. Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI. Comput Biol Med 2024; 169:107861. [PMID: 38141449 DOI: 10.1016/j.compbiomed.2023.107861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 12/25/2023]
Abstract
Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection without the demand to modify the existing model structures or to share any private data. Here, we proposed PPPML-HMI, a novel open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were discussed simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing the novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the strong privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we demonstrated the strong generalizability of PPPML-HMI in privacy-preserving federated learning on heterogeneous medical images.
Collapse
Affiliation(s)
- Juexiao Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Longxi Zhou
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Di Wang
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Xiaopeng Xu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Haoyang Li
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Yuetan Chu
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Wenkai Han
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia; Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Kingdom of Saudi Arabia.
| |
Collapse
|
34
|
Truhn D, Tayebi Arasteh S, Saldanha OL, Müller-Franzes G, Khader F, Quirke P, West NP, Gray R, Hutchins GGA, James JA, Loughrey MB, Salto-Tellez M, Brenner H, Brobeil A, Yuan T, Chang-Claude J, Hoffmeister M, Foersch S, Han T, Keil S, Schulze-Hagen M, Isfort P, Bruners P, Kaissis G, Kuhl C, Nebelung S, Kather JN. Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Med Image Anal 2024; 92:103059. [PMID: 38104402 PMCID: PMC10804934 DOI: 10.1016/j.media.2023.103059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 04/28/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.
Collapse
Affiliation(s)
- Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
| | - Soroosh Tayebi Arasteh
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Oliver Lester Saldanha
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Gustav Müller-Franzes
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Firas Khader
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philip Quirke
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Nicholas P West
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Richard Gray
- Clinical Trial Service Unit, University of Oxford, Oxford, United Kingdom
| | - Gordon G A Hutchins
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Jacqueline A James
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Maurice B Loughrey
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom; Department of Cellular Pathology, Belfast Health and Social Care Trust, Belfast, United Kingdom; Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom
| | - Manuel Salto-Tellez
- Precision Medicine Centre of Excellence, Health Sciences Building, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, United Kingdom; Regional Molecular Diagnostic Service, Belfast Health and Social Care Trust, Belfast, United Kingdom; The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, United Kingdom
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Alexander Brobeil
- Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Tissue Bank, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Jenny Chang-Claude
- Cancer Epidemiology Group, University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Tianyu Han
- Physics of Molecular Imaging Systems, Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany
| | - Sebastian Keil
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Maximilian Schulze-Hagen
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Peter Isfort
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Philipp Bruners
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Georgios Kaissis
- Institute of Diagnostic and Interventional Radiology, Technical University of Munich, Munich, Germany; Artificial Intelligence in Medicine and Healthcare, Technical University of Munich, Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom
| | - Christiane Kuhl
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Sven Nebelung
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany; Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom; Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| |
Collapse
|
35
|
Derraz B, Breda G, Kaempf C, Baenke F, Cotte F, Reiche K, Köhl U, Kather JN, Eskenazy D, Gilbert S. New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology. NPJ Precis Oncol 2024; 8:23. [PMID: 38291217 PMCID: PMC10828509 DOI: 10.1038/s41698-024-00517-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/06/2024] [Indexed: 02/01/2024] Open
Abstract
Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient's bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.
Collapse
Affiliation(s)
- Bouchra Derraz
- ProductLife Group, Paris, France
- Groupe de recherche et d'accueil en droit et économie de la santé (GRADES), Faculty of Pharmacy, University Paris-Saclay, Paris, France
| | | | - Christoph Kaempf
- Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
| | - Franziska Baenke
- Carl Gustav Carus University Hospital Dresden, Dresden University of Technology, Dresden, Germany
| | - Fabienne Cotte
- Department of Emergency Medicine, University Clinic Marburg, Philipps-University, Marburg, Germany
| | - Kristin Reiche
- Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany
- Institute of Clinical Immunology, University Leipzig, Leipzig, Germany
| | - Ulrike Köhl
- Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
- Institute of Clinical Immunology, University Leipzig, Leipzig, Germany
| | - Jakob Nikolas Kather
- Carl Gustav Carus University Hospital Dresden, Dresden University of Technology, Dresden, Germany
- Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany
| | - Deborah Eskenazy
- Groupe de recherche et d'accueil en droit et économie de la santé (GRADES), Faculty of Pharmacy, University Paris-Saclay, Paris, France
| | - Stephen Gilbert
- Carl Gustav Carus University Hospital Dresden, Dresden University of Technology, Dresden, Germany.
- Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
| |
Collapse
|
36
|
Leal Neto O, Von Wyl V. Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities. JMIR Public Health Surveill 2024; 10:e49575. [PMID: 38271097 PMCID: PMC10853859 DOI: 10.2196/49575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/13/2023] [Accepted: 12/12/2023] [Indexed: 01/27/2024] Open
Abstract
The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as "Assessment," "Policy Development," "Resource Allocation," "Assurance," and "Access." The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI's potential to enhance efficiency, effectiveness, user experience, and equity-the "quadruple aim"-is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI's ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.
Collapse
Affiliation(s)
- Onicio Leal Neto
- Department of Computer Science, ETH Zurich, Zurich, Switzerland
- Global Health Institute, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, United States
- Department of Epidemiology and Biostatistics, Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, United States
| | - Viktor Von Wyl
- Institute for Implementation Science in Health Care, University of Zurich, Zurich, Switzerland
- Epidemiology, Biostatistics & Prevention Institute, University of Zurich, Zurich, Switzerland
| |
Collapse
|
37
|
Klauschen F, Dippel J, Keyl P, Jurmeister P, Bockmayr M, Mock A, Buchstab O, Alber M, Ruff L, Montavon G, Müller KR. Toward Explainable Artificial Intelligence for Precision Pathology. ANNUAL REVIEW OF PATHOLOGY 2024; 19:541-570. [PMID: 37871132 DOI: 10.1146/annurev-pathmechdis-051222-113147] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.
Collapse
Affiliation(s)
- Frederick Klauschen
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Jonas Dippel
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
| | - Philipp Keyl
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Philipp Jurmeister
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Michael Bockmayr
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Pediatric Hematology and Oncology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Research Institute Children's Cancer Center Hamburg, Hamburg, Germany
| | - Andreas Mock
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
- German Cancer Consortium, German Cancer Research Center (DKTK/DKFZ), Munich Partner Site, Munich, Germany
| | - Oliver Buchstab
- Institute of Pathology, Ludwig-Maximilians-Universität München, Munich, Germany;
| | - Maximilian Alber
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
- Aignostics, Berlin, Germany
| | | | - Grégoire Montavon
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | - Klaus-Robert Müller
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
- Machine Learning Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany;
- Department of Artificial Intelligence, Korea University, Seoul, Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
| |
Collapse
|
38
|
Liu GY, Yu D, Fan MM, Zhang X, Jin ZY, Tang C, Liu XF. Antimicrobial resistance crisis: could artificial intelligence be the solution? Mil Med Res 2024; 11:7. [PMID: 38254241 PMCID: PMC10804841 DOI: 10.1186/s40779-024-00510-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Antimicrobial resistance is a global public health threat, and the World Health Organization (WHO) has announced a priority list of the most threatening pathogens against which novel antibiotics need to be developed. The discovery and introduction of novel antibiotics are time-consuming and expensive. According to WHO's report of antibacterial agents in clinical development, only 18 novel antibiotics have been approved since 2014. Therefore, novel antibiotics are critically needed. Artificial intelligence (AI) has been rapidly applied to drug development since its recent technical breakthrough and has dramatically improved the efficiency of the discovery of novel antibiotics. Here, we first summarized recently marketed novel antibiotics, and antibiotic candidates in clinical development. In addition, we systematically reviewed the involvement of AI in antibacterial drug development and utilization, including small molecules, antimicrobial peptides, phage therapy, essential oils, as well as resistance mechanism prediction, and antibiotic stewardship.
Collapse
Affiliation(s)
- Guang-Yu Liu
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Dan Yu
- National Key Discipline of Pediatrics Key Laboratory of Major Diseases in Children Ministry of Education, Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Mei-Mei Fan
- Department of Immunology and Pathogen Biology, School of Basic Medical Sciences, Hangzhou Normal University, Key Laboratory of Aging and Cancer Biology of Zhejiang Province, Key Laboratory of Inflammation and Immunoregulation of Hangzhou, Hangzhou Normal University, Hangzhou, 311121, China
| | - Xu Zhang
- Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Biochemistry and Molecular Biology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ze-Yu Jin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Christoph Tang
- Sir William Dunn School of Pathology, University of Oxford, Oxford, OX1 3RE, UK.
| | - Xiao-Fen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Key Laboratory of Clinical Pharmacology of Antibiotics, National Health Commission of the People's Republic of China, National Clinical Research Centre for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
| |
Collapse
|
39
|
Baumgart DC. An intriguing vision for transatlantic collaborative health data use and artificial intelligence development. NPJ Digit Med 2024; 7:19. [PMID: 38263436 PMCID: PMC10806986 DOI: 10.1038/s41746-024-01005-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
Our traditional approach to diagnosis, prognosis, and treatment, can no longer process and transform the enormous volume of information into therapeutic success, innovative discovery, and health economic performance. Precision health, i.e., the right treatment, for the right person, at the right time in the right place, is enabled through a learning health system, in which medicine and multidisciplinary science, economic viability, diverse culture, and empowered patient's preferences are digitally integrated and conceptually aligned for continuous improvement and maintenance of health, wellbeing, and equity. Artificial intelligence (AI) has been successfully evaluated in risk stratification, accurate diagnosis, and treatment allocation, and to prevent health disparities. There is one caveat though: dependable AI models need to be trained on population-representative, large and deep data sets by multidisciplinary and multinational teams to avoid developer, statistical and social bias. Such applications and models can neither be created nor validated with data at the country, let alone institutional level and require a new dimension of collaboration, a cultural change with the establishment of trust in a precompetitive space. The Data for Health (#DFH23) conference in Berlin and the Follow-Up Workshop at Harvard University in Boston hosted a representative group of stakeholders in society, academia, industry, and government. With the momentum #DFH23 created, the European Health Data Space (EHDS) as a solid and safe foundation for consented collaborative health data use and the G7 Hiroshima AI process in place, we call on citizens and their governments to fully support digital transformation of medicine, research and innovation including AI.
Collapse
Affiliation(s)
- Daniel C Baumgart
- Precision Health Signature Area, College of Health Sciences, College of Natural and Applied Sciences all at University of Alberta, Edmonton, Alberta, Canada.
| |
Collapse
|
40
|
Guo ZH, Wang YB, Wang S, Zhang Q, Huang DS. scCorrector: a robust method for integrating multi-study single-cell data. Brief Bioinform 2024; 25:bbad525. [PMID: 38271483 PMCID: PMC10810333 DOI: 10.1093/bib/bbad525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/12/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024] Open
Abstract
The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.
Collapse
Affiliation(s)
- Zhen-Hao Guo
- College of Electronics and Information Engineering, Tongji University, Shanghai 200000, China
| | - Yan-Bin Wang
- College of Computer Science and Technology, Zhejiang University 310027, China
| | - Siguo Wang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| | - Qinhu Zhang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| | - De-Shuang Huang
- Eastern Institute for Advanced Study, Eastern Institute of Technology, Tongxin Road No.568, Ningbo, Zhejiang 315201, China
| |
Collapse
|
41
|
Walton NA, Nagarajan R, Wang C, Sincan M, Freimuth RR, Everman DB, Walton DC, McGrath SP, Lemas DJ, Benos PV, Alekseyenko AV, Song Q, Gamsiz Uzun E, Taylor CO, Uzun A, Person TN, Rappoport N, Zhao Z, Williams MS. Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup. J Am Med Inform Assoc 2024; 31:536-541. [PMID: 38037121 PMCID: PMC10797281 DOI: 10.1093/jamia/ocad211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 10/09/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023] Open
Abstract
OBJECTIVE Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.
Collapse
Affiliation(s)
- Nephi A Walton
- Division of Medical Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112 ,United States
| | - Radha Nagarajan
- Enterprise Information Services, Cedars-Sinai Medical Center, Los Angeles, CA 90025, United States
- Information Services Department, Children’s Hospital of Orange County, Orange, CA 92868, United States
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - Murat Sincan
- Flatiron Health, New York, NY 10013, United States
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD 57107, United States
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States
| | - David B Everman
- EverMed Genetics and Genomics Consulting LLC, Greenville, SC 29607, United States
| | | | - Scott P McGrath
- CITRIS Health, CITRIS and Banatao Institute, University of California Berkeley, Berkeley, CA 94720, United States
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, United States
| | - Alexander V Alekseyenko
- Department of Public Health Sciences, Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29403, United States
| | - Qianqian Song
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610, United States
| | - Ece Gamsiz Uzun
- Department of Pathology and Laboratory Medicine, Rhode Island Hospital and Lifespan Medical Center, Providence, RI 02915, United States
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
| | - Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, United States
| | - Alper Uzun
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI 02915, United States
- Legorreta Cancer Center, Brown University, Providence, RI 02915, United States
| | - Thomas Nate Person
- Department of Bioinformatics and Genomics, Huck Institutes of the Life Sciences, Penn State University, Bloomsburg, PA 16802, United States
| | - Nadav Rappoport
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva 8410501, Israel
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Marc S Williams
- Department of Genomic Health, Geisinger, Danville, PA 17822, United States
| |
Collapse
|
42
|
Shukla M, Seneviratne O. MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:641-652. [PMID: 38222418 PMCID: PMC10785875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. To address this issue, we propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices. Our system leverages a decentralized learning mechanism that combines transfer and federated machine learning concepts using smart contracts, allowing data to remain on users' devices and enabling effective tracking of mental health conditions for psychiatric treatment and management in a privacy-aware and accountable manner. We evaluated our model using a popular mental health dataset, which yielded promising results. By utilizing connected health systems and machine learning models, our approach offers a novel solution to the challenge of providing psychiatrists with further insight into their patients' mental health outside of traditional office visits.
Collapse
|
43
|
Welten S, Weber S, Holt A, Beyan O, Decker S. Will it run?-A proof of concept for smoke testing decentralized data analytics experiments. Front Med (Lausanne) 2024; 10:1305415. [PMID: 38259836 PMCID: PMC10801058 DOI: 10.3389/fmed.2023.1305415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/14/2023] [Indexed: 01/24/2024] Open
Abstract
The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.
Collapse
Affiliation(s)
- Sascha Welten
- Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Sven Weber
- Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Adrian Holt
- Chair of Computer Science 5, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Oya Beyan
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Fraunhofer Institute for Applied Information Technology FIT, St. Augustin, Germany
| | - Stefan Decker
- Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Fraunhofer Institute for Applied Information Technology FIT, St. Augustin, Germany
| |
Collapse
|
44
|
Clough E, Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim I, Tomashevsky M, Marshall K, Phillippy K, Sherman P, Lee H, Zhang N, Serova N, Wagner L, Zalunin V, Kochergin A, Soboleva A. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res 2024; 52:D138-D144. [PMID: 37933855 PMCID: PMC10767856 DOI: 10.1093/nar/gkad965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The Gene Expression Omnibus (GEO) is an international public repository that archives gene expression and epigenomics data sets generated by next-generation sequencing and microarray technologies. Data are typically submitted to GEO by researchers in compliance with widespread journal and funder mandates to make generated data publicly accessible. The resource handles raw data files, processed data files and descriptive metadata for over 200 000 studies and 6.5 million samples, all of which are indexed, searchable and downloadable. Additionally, GEO offers web-based tools that facilitate analysis and visualization of differential gene expression. This article presents the current status and recent advancements in GEO, including the generation of consistently computed gene expression count matrices for thousands of RNA-seq studies, and new interactive graphical plots in GEO2R that help users identify differentially expressed genes and assess data set quality. The GEO repository is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), and is publicly accessible at https://www.ncbi.nlm.nih.gov/geo/.
Collapse
Affiliation(s)
- Emily Clough
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Tanya Barrett
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Stephen E Wilhite
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Pierre Ledoux
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Carlos Evangelista
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Irene F Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Maxim Tomashevsky
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kimberly A Marshall
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Katherine H Phillippy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patti M Sherman
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Hyeseung Lee
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Naigong Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nadezhda Serova
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lukas Wagner
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Vadim Zalunin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Andrey Kochergin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Alexandra Soboleva
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| |
Collapse
|
45
|
Bernier A, Molnár-Gábor F, Knoppers BM, Borry P, Cesar PMDG, Devriendt T, Goisauf M, Murtagh M, Jiménez PN, Recuero M, Rial-Sebbag E, Shabani M, Wilson RC, Zaccagnini D, Maxwell L. Reconciling the biomedical data commons and the GDPR: three lessons from the EUCAN ELSI collaboratory. Eur J Hum Genet 2024; 32:69-76. [PMID: 37322132 PMCID: PMC10267538 DOI: 10.1038/s41431-023-01403-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/26/2023] [Accepted: 05/24/2023] [Indexed: 06/17/2023] Open
Abstract
The coming-into-force of the EU General Data Protection Regulation (GDPR) is a watershed moment in the legal recognition of enforceable rights to informational self-determination. The rapid evolution of legal requirements applicable to data use, however, has the potential to outstrip the capabilities of networks of biomedical data users to respond to the shifting norms. It can also delegitimate established institutional bodies that are responsible for assessing and authorising the downstream use of data, including research ethics committees and institutional data custodians. These burdens are especially pronounced for clinical and research networks that are of transnational scale, because the legal compliance burden for outbound international data transfers from the EEA is especially high. Legislatures, courts, and regulators in the EU should therefore implement the following three legal changes. First, the responsibilities of particular actors in a data sharing network should be delimited through the contractual allocation of responsibilities between collaborators. Second, the use of data through secure data processing environments should not trigger the international transfer provisions of the GDPR. Third, the use of federated data analysis methodologies that do not provide analysis nodes or downstream users access to identifiable personal data as part of the outputs of those analyses should not be considered circumstances of joint controllership, nor lead to the users of non-identifiable data to be considered controllers or processors. These small clarifications of, or modifications to, the GDPR would facilitate the exchange of biomedical data amongst clinicians and researchers.
Collapse
Affiliation(s)
- Alexander Bernier
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain.
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain.
- Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences, Montréal, QC, Canada.
| | - Fruzsina Molnár-Gábor
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- Heidelberg Academy of Sciences and Humanities, Heidelberg University, Heidelberg, Germany
| | - Bartha M Knoppers
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Centre of Genomics and Policy, McGill University Faculty of Medicine and Health Sciences, Montréal, QC, Canada
| | - Pascal Borry
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Priscilla M D G Cesar
- Institute on Ethics & Policy for Innovation (IEPI), McMaster University, Hamilton, ON, Canada
- RECODID: Reconciliation of Cohort Data in Infectious Diseases, Heidelberg, Germany
| | - Thijs Devriendt
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Melanie Goisauf
- ELSI Services & Research, BBMRI-ERIC, Graz, Austria
- CINECA: Common Infrastructure for International Cohorts in Europe, Canada, and Africa, Heidelberg, Germany
| | - Madeleine Murtagh
- EUCAN-Connect: Federated, FAIR Platform Enabling Large-Scale Analysis of High-Value Cohort Data Connecting Europe and Canada in Personalized Health, Groningen, the Netherlands
- School of Social and Political Studies, University of Glasgow, Glasgow, Scotland, UK
| | - Pilar Nicolás Jiménez
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- EuCanImage: A European Cancer Image Platform Linked to Biological and Health Data for Next Generation Artificial Intelligence and Precision Medicine in Oncology, Barcelona, Spain
- Social and Legal Sciences Applied to the New Technosciences Research Group, Faculty of Law, University of the Basque Country, Bilbao, Spain
| | - Mikel Recuero
- EUCANCan: European-Canadian Cancer Network, Barcelona, Spain
- EuCanImage: A European Cancer Image Platform Linked to Biological and Health Data for Next Generation Artificial Intelligence and Precision Medicine in Oncology, Barcelona, Spain
- Social and Legal Sciences Applied to the New Technosciences Research Group, Faculty of Law, University of the Basque Country, Bilbao, Spain
| | - Emmanuelle Rial-Sebbag
- CINECA: Common Infrastructure for International Cohorts in Europe, Canada, and Africa, Heidelberg, Germany
- CERPOP, Inserm, Toulouse Paul Sabatier University, Toulouse, France
| | - Mahsa Shabani
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Metamedica, Faculty of Law and Criminology, Ghent University, Ghent, Belgium
| | - Rebecca C Wilson
- EUCAN-Connect: Federated, FAIR Platform Enabling Large-Scale Analysis of High-Value Cohort Data Connecting Europe and Canada in Personalized Health, Groningen, the Netherlands
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Davide Zaccagnini
- euCanSHare: An EU-Canada Joint Infrastructure for Next-Generation Multi-Heart Research, Barcelona, Spain
- Lynkeus S.R.L, Roma, Italy
| | - Lauren Maxwell
- RECODID: Reconciliation of Cohort Data in Infectious Diseases, Heidelberg, Germany
- Heidelberg Institute for Global Health, Heidelberg University, Im Neuenheimer Feld 130/3, 69120, Heidelberg, Germany
| |
Collapse
|
46
|
Zhang J, Zou H. Insights into artificial intelligence in myopia management: from a data perspective. Graefes Arch Clin Exp Ophthalmol 2024; 262:3-17. [PMID: 37231280 PMCID: PMC10212230 DOI: 10.1007/s00417-023-06101-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 03/23/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023] Open
Abstract
Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.
Collapse
Affiliation(s)
- Juzhao Zhang
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haidong Zou
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Eye Diseases Prevention & Treatment Center, Shanghai Eye Hospital, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
| |
Collapse
|
47
|
Matsumoto H, Ogura H, Oda J. Analysis of comprehensive biomolecules in critically ill patients via bioinformatics technologies. Acute Med Surg 2024; 11:e944. [PMID: 38596160 PMCID: PMC11002317 DOI: 10.1002/ams2.944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 02/23/2024] [Accepted: 03/10/2024] [Indexed: 04/11/2024] Open
Abstract
Each patient with a critical illness such as sepsis and severe trauma has a different genetic background, comorbidities, age, and sex. Moreover, pathophysiology changes dynamically over time even in the same patient. Therefore, individualized treatment is necessary to account for heterogeneity in patient backgrounds. Recently, the analysis of comprehensive biomolecular information using clinical specimens has revealed novel molecular pathological classifications called subtypes. In addition, comprehensive biomolecular information using clinical specimens has enabled reverse translational research, which is a data-driven approach to the identification of drug target molecules. The development of these methods is expected to visualize the heterogeneity of patient backgrounds and lead to personalized therapy.
Collapse
Affiliation(s)
- Hisatake Matsumoto
- Department of Traumatology and Acute Critical MedicineOsaka University Graduate School of MedicineSuitaOsakaJapan
| | - Hiroshi Ogura
- Department of Traumatology and Acute Critical MedicineOsaka University Graduate School of MedicineSuitaOsakaJapan
| | - Jun Oda
- Department of Traumatology and Acute Critical MedicineOsaka University Graduate School of MedicineSuitaOsakaJapan
| |
Collapse
|
48
|
Ma S, Chen J, Ho JWK. An edge-device-compatible algorithm for valvular heart diseases screening using phonocardiogram signals with a lightweight convolutional neural network and self-supervised learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107906. [PMID: 37950925 DOI: 10.1016/j.cmpb.2023.107906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/24/2023] [Accepted: 10/27/2023] [Indexed: 11/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Detection and classification of heart murmur using mobile-phone-collected sound is an emerging approach to the scale-up screening of valvular heart disease at a population level. Nonetheless, the widespread adoption of artificial intelligence (AI) methods for this type of mobile health (mHealth) application requires highly accurate and lightweight AI models that can be deployed in consumer-grade mobile devices. This study presents a lightweight deep learning model and a self-supervised learning (SSL) method to utilise unlabelled data to improve the accuracy of valvular heart disease classification using phonocardiogram data. METHODS This study proposes a lightweight convolutional neural network (CNN) that consists of ten times fewer parameters than other deep learning models to classify phonocardiogram data. SSL is applied to harness a large collection of unlabelled data as pre-training to enhance the accuracy and robustness of the model and reduce the number of epochs required to converge. A mobile application prototype that encapsulates the model is developed to perform in-device inference and fine-turning. RESULTS The proposed lightweight model achieves an average accuracy of 98.65% in 10-fold cross-validation. When coupled with SSL using unlabelled data, the pre-trained model can reach an average accuracy higher than 99.4% in 10-fold cross-validation. Furthermore, SSL-trained models have a 4-20% improvement in classification accuracy over non-SSL-trained models when tested with perturbed or noisy data, suggesting that SSL improves robustness of the model. When deployed on common smartphones, in-device fine-tuning and inference of the model can be completed within 0.03-0.37 s, which is considerably faster than 0.22-5.7 s by a standard CNN model that have ten times the number of parameters. Our lightweight model also consumes only a third of the power compared to the larger standard model. CONCLUSION This work presents a lightweight and accurate phonocardiogram classifier that supports near real-time performance on standard mobile devices.
Collapse
Affiliation(s)
- Shichao Ma
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Junyi Chen
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China; Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China.
| |
Collapse
|
49
|
Schultze JL. Building Trust in Medical Use of Artificial Intelligence – The Swarm Learning Principle. JOURNAL OF CME 2023; 12:2162202. [PMID: 36969482 PMCID: PMC10031775 DOI: 10.1080/28338073.2022.2162202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
An avalanche of medical data is starting to be build up. With the digitalisation of medicine and novel approaches such as the omics technologies, we are conquering ever bigger data spaces to be used to describe pathophysiology of diseases, define biomarkers for diagnostic purposes or identify novel drug targets. Utilising this growing lake of medical data will only be possible, if we make use of machine learning, in particular artificial intelligence (AI)-based algorithms. While the technological developments and chances of the data and information sciences are enormous, the use of AI in medicine also bears challenges and many of the current information technologies (IT) do not follow established medical traditions of mentoring, learning together, sharing insights, while preserving patient's data privacy by patient physician privilege. Other challenges to the medical sector are demands from the scientific community such as "Open Science", "Open Data", "Open Access" principles. A major question to be solved is how to guide technological developments in the IT sector to serve well-established medical traditions and processes, yet allow medicine to benefit from the many advantages of state-of-the-art IT. Here, I provide the Swarm Learning (SL) principle as a conceptual framework designed to foster medical standards, processes and traditions. A major difference to current IT solutions is the inherent property of SL to appreciate and acknowledge existing regulations in medicine that have been proven beneficial for patients and medical personal alike for centuries.
Collapse
Affiliation(s)
- Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
- PRECISE Platform for Single Cell Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) and University of Bonn, Bonn, Germany
- Genomics & Immunoregulation, sLife and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
| |
Collapse
|
50
|
Zhang B, Zhang L, Chen Q, Jin Z, Liu S, Zhang S. Harnessing artificial intelligence to improve clinical trial design. COMMUNICATIONS MEDICINE 2023; 3:191. [PMID: 38129570 PMCID: PMC10739942 DOI: 10.1038/s43856-023-00425-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
Zhang et al. discuss how artificial intelligence (AI) can be used to optimize clinical trial design and potentially boost the success rate of clinical trials. AI has unparalleled potential to leverage real-world data and unlock valuable insights for innovative trial design.
Collapse
Affiliation(s)
- Bin Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Lu Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Qiuying Chen
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Zhe Jin
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Shuyi Liu
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China
| | - Shuixing Zhang
- The First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China.
| |
Collapse
|