1
|
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
|
2
|
Greeley B, Chung SS, Graves L, Song X. Combating Barriers to the Development of a Patient-Oriented Frailty Website. JMIR Aging 2024; 7:e53098. [PMID: 38807317 DOI: 10.2196/53098] [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: 09/25/2023] [Revised: 01/02/2024] [Accepted: 03/07/2024] [Indexed: 05/30/2024] Open
Abstract
Unlabelled This viewpoint article, which represents the opinions of the authors, discusses the barriers to developing a patient-oriented frailty website and potential solutions. A patient-oriented frailty website is a health resource where community-dwelling older adults can navigate to and answer a series of health-related questions to receive a frailty score and health summary. This information could then be shared with health care professionals to help with the understanding of health status prior to acute illness, as well as to screen and identify older adult individuals for frailty. Our viewpoints were drawn from 2 discussion sessions that included caregivers and care providers, as well as community-dwelling older adults. We found that barriers to a patient-oriented frailty website include, but are not limited to, its inherent restrictiveness to frail persons, concerns over data privacy, time commitment worries, and the need for health and lifestyle resources in addition to an assessment summary. For each barrier, we discuss potential solutions and caveats to those solutions, including assistance from caregivers, hosting the website on a trusted source, reducing the number of health questions that need to be answered, and providing resources tailored to each users' responses, respectively. In addition to screening and identifying frail older adults, a patient-oriented frailty website will help promote healthy aging in nonfrail adults, encourage aging in place, support real-time monitoring, and enable personalized and preventative care.
Collapse
|
3
|
Naik K, Goyal RK, Foschini L, Chak CW, Thielscher C, Zhu H, Lu J, Lehár J, Pacanoswki MA, Terranova N, Mehta N, Korsbo N, Fakhouri T, Liu Q, Gobburu J. Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine. Clin Pharmacol Ther 2024; 115:673-686. [PMID: 38103204 DOI: 10.1002/cpt.3152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled "Application of Artificial Intelligence and Machine Learning for Precision Medicine," hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies - some of which are highlighted presentations from the workshop - are for the protection of personal information and intellectual property. In addition, methods such as the risk-based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad-spectrum of stakeholders to enhance the application of AI in precision medicine.
Collapse
Affiliation(s)
- Kunal Naik
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rahul K Goyal
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | | | | | | | - Hao Zhu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - James Lu
- Modeling & Simulation/Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | | | - Michael A Pacanoswki
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nadia Terranova
- Quantitative Pharmacology, Ares Trading S.A. (an affiliate of Merck KGaA, Darmstadt, Germany), Lausanne, Switzerland
| | - Neha Mehta
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Tala Fakhouri
- Office of Medical Policy, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qi Liu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jogarao Gobburu
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| |
Collapse
|
4
|
Nguyen TPV, Yang W, Tang Z, Xia X, Mullens AB, Dean JA, Li Y. Lightweight federated learning for STIs/HIV prediction. Sci Rep 2024; 14:6560. [PMID: 38503789 PMCID: PMC10950866 DOI: 10.1038/s41598-024-56115-0] [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: 12/01/2023] [Accepted: 03/01/2024] [Indexed: 03/21/2024] Open
Abstract
This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man's data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework's impact and ultimately aim to improve health outcomes/health service optimisation.
Collapse
Affiliation(s)
- Thi Phuoc Van Nguyen
- School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, QLD, Australia.
| | - Wencheng Yang
- School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, QLD, Australia
| | - Zhaohui Tang
- School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, QLD, Australia
| | - Xiaoyu Xia
- School of Computing Technologies, RMIT University, GPO Box 2476, Melbourne, 3001, VIC, Australia
| | - Amy B Mullens
- School of Psychology and Wellbeing, Institute for Resilient Regions, Centre for Health Research, University of Southern Queensland, Ipswich Campus, Ipswich, 4305, Australia
| | - Judith A Dean
- School of Public Health, Faculty of Medicine, The University of Queensland, Herston Road, Brisbane, 4006, QLD, Australia
| | - Yan Li
- School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350, QLD, Australia
| |
Collapse
|
5
|
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
|
6
|
Wu X, Li W, Tu H. Big data and artificial intelligence in cancer research. Trends Cancer 2024; 10:147-160. [PMID: 37977902 DOI: 10.1016/j.trecan.2023.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.
Collapse
Affiliation(s)
- Xifeng Wu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
| |
Collapse
|
7
|
Choi G, Cha WC, Lee SU, Shin SY. Survey of Medical Applications of Federated Learning. Healthc Inform Res 2024; 30:3-15. [PMID: 38359845 PMCID: PMC10879826 DOI: 10.4258/hir.2024.30.1.3] [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: 10/05/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/17/2024] Open
Abstract
OBJECTIVES Medical artificial intelligence (AI) has recently attracted considerable attention. However, training medical AI models is challenging due to privacy-protection regulations. Among the proposed solutions, federated learning (FL) stands out. FL involves transmitting only model parameters without sharing the original data, making it particularly suitable for the medical field, where data privacy is paramount. This study reviews the application of FL in the medical domain. METHODS We conducted a literature search using the keywords "federated learning" in combination with "medical," "healthcare," or "clinical" on Google Scholar and PubMed. After reviewing titles and abstracts, 58 papers were selected for analysis. These FL studies were categorized based on the types of data used, the target disease, the use of open datasets, the local model of FL, and the neural network model. We also examined issues related to heterogeneity and security. RESULTS In the investigated FL studies, the most commonly used data type was image data, and the most studied target diseases were cancer and COVID-19. The majority of studies utilized open datasets. Furthermore, 72% of the FL articles addressed heterogeneity issues, while 50% discussed security concerns. CONCLUSIONS FL in the medical domain appears to be in its early stages, with most research using open data and focusing on specific data types and diseases for performance verification purposes. Nonetheless, medical FL research is anticipated to be increasingly applied and to become a vital component of multi-institutional research.
Collapse
Affiliation(s)
- Geunho Choi
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul,
Korea
| | - Won Chul Cha
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul,
Korea
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul,
Korea
| | - Se Uk Lee
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul,
Korea
| | - Soo-Yong Shin
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul,
Korea
| |
Collapse
|
8
|
Liu Y, Bi D. Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2422-2449. [PMID: 36906293 DOI: 10.1111/risa.14124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients' responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients' responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.
Collapse
Affiliation(s)
- Yang Liu
- School of Economics and Management, Dalian University of Technology, Dalian, China
| | - Donghai Bi
- School of Economics and Management, Dalian University of Technology, Dalian, China
| |
Collapse
|
9
|
Li S, Liu P, Nascimento GG, Wang X, Leite FRM, Chakraborty B, Hong C, Ning Y, Xie F, Teo ZL, Ting DSW, Haddadi H, Ong MEH, Peres MA, Liu N. Federated and distributed learning applications for electronic health records and structured medical data: a scoping review. J Am Med Inform Assoc 2023; 30:2041-2049. [PMID: 37639629 PMCID: PMC10654866 DOI: 10.1093/jamia/ocad170] [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: 05/19/2023] [Revised: 07/19/2023] [Indexed: 08/31/2023] Open
Abstract
OBJECTIVES Federated learning (FL) has gained popularity in clinical research in recent years to facilitate privacy-preserving collaboration. Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice. This review examines FL applications on structured medical data, identifies contemporary limitations, and discusses potential innovations. MATERIALS AND METHODS We searched 5 databases, SCOPUS, MEDLINE, Web of Science, Embase, and CINAHL, to identify articles that applied FL to structured medical data and reported results following the PRISMA guidelines. Each selected publication was evaluated from 3 primary perspectives, including data quality, modeling strategies, and FL frameworks. RESULTS Out of the 1193 papers screened, 34 met the inclusion criteria, with each article consisting of one or more studies that used FL to handle structured clinical/medical data. Of these, 24 utilized data acquired from electronic health records, with clinical predictions and association studies being the most common clinical research tasks that FL was applied to. Only one article exclusively explored the vertical FL setting, while the remaining 33 explored the horizontal FL setting, with only 14 discussing comparisons between single-site (local) and FL (global) analysis. CONCLUSIONS The existing FL applications on structured medical data lack sufficient evaluations of clinically meaningful benefits, particularly when compared to single-site analyses. Therefore, it is crucial for future FL applications to prioritize clinical motivations and develop designs and methodologies that can effectively support and aid clinical practice and research.
Collapse
Affiliation(s)
- Siqi Li
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Pinyan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Gustavo G Nascimento
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore 168938, Singapore
- Oral Health Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Xinru Wang
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Fabio Renato Manzolli Leite
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore 168938, Singapore
- Oral Health Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Bibhas Chakraborty
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
- Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, United States
| | - Chuan Hong
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, United States
| | - Yilin Ning
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Feng Xie
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore, Singapore Eye Research Institute, Singapore 168751, Singapore
| | - Daniel Shu Wei Ting
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
- Singapore National Eye Centre, Singapore, Singapore Eye Research Institute, Singapore 168751, Singapore
| | - Hamed Haddadi
- Department of Computing, Imperial College London, London SW7 2AZ, England, United Kingdom
| | - Marcus Eng Hock Ong
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
- Department of Emergency Medicine, Singapore General Hospital, Singapore 169608, Singapore
| | - Marco Aurélio Peres
- National Dental Research Institute Singapore, National Dental Centre Singapore, Singapore 168938, Singapore
- Oral Health Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Nan Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore 169857, Singapore
- Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore 169857, Singapore
- Institute of Data Science, National University of Singapore, Singapore 117602, Singapore
| |
Collapse
|
10
|
Ran AR, Wang X, Chan PP, Wong MOM, Yuen H, Lam NM, Chan NCY, Yip WWK, Young AL, Yung HW, Chang RT, Mannil SS, Tham YC, Cheng CY, Wong TY, Pang CP, Heng PA, Tham CC, Cheung CY. Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning. Br J Ophthalmol 2023:bjo-2023-324188. [PMID: 37857452 DOI: 10.1136/bjo-2023-324188] [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: 07/03/2023] [Accepted: 09/23/2023] [Indexed: 10/21/2023]
Abstract
BACKGROUND Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) paradigm to detect glaucoma from optical coherence tomography (OCT) images. METHODS This is a multicentre study. The FL paradigm consisted of a 'central server' and seven eye centres in Hong Kong, the USA and Singapore. Each centre first trained a model locally with its own OCT optic disc volumetric dataset and then uploaded its model parameters to the central server. The central server used FedProx algorithm to aggregate all centres' model parameters. Subsequently, the aggregated parameters are redistributed to each centre for its local model optimisation. We experimented with three three-dimensional (3D) networks to evaluate the stabilities of the FL paradigm. Lastly, we tested the FL model on two prospectively collected unseen datasets. RESULTS We used 9326 volumetric OCT scans from 2785 subjects. The FL model performed consistently well with different networks in 7 centres (accuracies 78.3%-98.5%, 75.9%-97.0%, and 78.3%-97.5%, respectively) and stably in the 2 unseen datasets (accuracies 84.8%-87.7%, 81.3%-84.8%, and 86.0%-87.8%, respectively). The FL model achieved non-inferior performance in classifying glaucoma compared with the traditional model and significantly outperformed the individual models. CONCLUSION The 3D FL model could leverage all the datasets and achieve generalisable performance, without data exchange across centres. This study demonstrated an OCT-based FL paradigm for glaucoma identification with ensured patient privacy and data security, charting another course toward the real-world transition of artificial intelligence in ophthalmology.
Collapse
Affiliation(s)
- An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xi Wang
- Zhejiang Lab, Hangzhou, China
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, California, USA
| | - Poemen P Chan
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | | | - Hunter Yuen
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | - Nai Man Lam
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | - Noel C Y Chan
- Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
- Ophthalmology and Visual Sciences, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China
| | - Wilson W K Yip
- Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
- Ophthalmology and Visual Sciences, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China
| | - Alvin L Young
- Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong SAR, China
- Ophthalmology and Visual Sciences, Alice Ho Miu Ling Nethersole Hospital, Hong Kong SAR, China
| | | | - Robert T Chang
- Ophthalmology, Stanford University School of Medicine, Stanford, California, USA
| | - Suria S Mannil
- Ophthalmology, Stanford University School of Medicine, Stanford, California, USA
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Duke-National University of Singapore Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Chi Pui Pang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong SAR, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
11
|
Gholami S, Lim JI, Leng T, Ong SSY, Thompson AC, Alam MN. Federated learning for diagnosis of age-related macular degeneration. Front Med (Lausanne) 2023; 10:1259017. [PMID: 37901412 PMCID: PMC10613107 DOI: 10.3389/fmed.2023.1259017] [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: 07/14/2023] [Accepted: 09/25/2023] [Indexed: 10/31/2023] Open
Abstract
This paper presents a federated learning (FL) approach to train deep learning models for classifying age-related macular degeneration (AMD) using optical coherence tomography image data. We employ the use of residual network and vision transformer encoders for the normal vs. AMD binary classification, integrating four unique domain adaptation techniques to address domain shift issues caused by heterogeneous data distribution in different institutions. Experimental results indicate that FL strategies can achieve competitive performance similar to centralized models even though each local model has access to a portion of the training data. Notably, the Adaptive Personalization FL strategy stood out in our FL evaluations, consistently delivering high performance across all tests due to its additional local model. Furthermore, the study provides valuable insights into the efficacy of simpler architectures in image classification tasks, particularly in scenarios where data privacy and decentralization are critical using both encoders. It suggests future exploration into deeper models and other FL strategies for a more nuanced understanding of these models' performance. Data and code are available at https://github.com/QIAIUNCC/FL_UNCC_QIAI.
Collapse
Affiliation(s)
- Sina Gholami
- Department of Electrical Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States
| | - Jennifer I. Lim
- Department of Ophthalmology and Visual Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Theodore Leng
- Department of Ophthalmology, School of Medicine, Stanford University, Stanford, CA, United States
| | - Sally Shin Yee Ong
- Department of Surgical Ophthalmology, Atrium-Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Atalie Carina Thompson
- Department of Surgical Ophthalmology, Atrium-Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Minhaj Nur Alam
- Department of Electrical Engineering, University of North Carolina at Charlotte, Charlotte, NC, United States
| |
Collapse
|
12
|
Lysaght T, Chan HY, Scheibner J, Toh HJ, Richards B. An ethical code for collecting, using and transferring sensitive health data: outcomes of a modified Policy Delphi process in Singapore. BMC Med Ethics 2023; 24:78. [PMID: 37794387 PMCID: PMC10552227 DOI: 10.1186/s12910-023-00952-7] [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: 04/11/2023] [Accepted: 09/06/2023] [Indexed: 10/06/2023] Open
Abstract
One of the core goals of Digital Health Technologies (DHT) is to transform healthcare services and delivery by shifting primary care from hospitals into the community. However, achieving this goal will rely on the collection, use and storage of large datasets. Some of these datasets will be linked to multiple sources, and may include highly sensitive health information that needs to be transferred across institutional and jurisdictional boundaries. The growth of DHT has outpaced the establishment of clear legal pathways to facilitate the collection, use and transfer of potentially sensitive health data. Our study aimed to address this gap with an ethical code to guide researchers developing DHT with international collaborative partners in Singapore. We generated this code using a modified Policy Delphi process designed to engage stakeholders in the deliberation of health data ethics and governance. This paper reports the outcomes of this process along with the key components of the code and identifies areas for future research.
Collapse
Affiliation(s)
- Tamra Lysaght
- Centre for Biomedical Ethics, Clinical Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Level 2 Block MD11, 10 Medical Drive, Singapore, 117597, Singapore
| | - Hui Yun Chan
- Centre for Biomedical Ethics, Clinical Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Level 2 Block MD11, 10 Medical Drive, Singapore, 117597, Singapore.
| | - James Scheibner
- College of Business, Government & Law, Flinders University, Ring Road, Bedford Park South Australia 5042, GPO Box 2100, Adelaide, South Australia, 5001, Australia
| | - Hui Jin Toh
- Centre for Biomedical Ethics, Clinical Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Level 2 Block MD11, 10 Medical Drive, Singapore, 117597, Singapore
| | - Bernadette Richards
- Academy for Medical Education, Medical School, The University of Queensland, 288 Herston Rd, Herston, QLD, 4006, Australia
| |
Collapse
|
13
|
Li W, Kim M, Zhang K, Chen H, Jiang X, Harmanci A. COLLAGENE enables privacy-aware federated and collaborative genomic data analysis. Genome Biol 2023; 24:204. [PMID: 37697426 PMCID: PMC10496350 DOI: 10.1186/s13059-023-03039-z] [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: 12/26/2022] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .
Collapse
Affiliation(s)
- Wentao Li
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Miran Kim
- Department of Mathematics, Department of Computer Science, Hanyang University, Seoul, 04763, Republic of Korea
- Research Institute for Convergence of Basic Science, Hanyang University, Seoul, 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, 04763, Republic of Korea
| | - Kai Zhang
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Center for Precision Health, D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Xiaoqian Jiang
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA
| | - Arif Harmanci
- Center for Secure Artificial Intelligence For hEalthcare (SAFE), D. Bradley McWilliams School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, 77030, USA.
- Center for Precision Health, D. Bradley McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
| |
Collapse
|
14
|
Chen RJ, Wang JJ, Williamson DFK, Chen TY, Lipkova J, Lu MY, Sahai S, Mahmood F. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng 2023; 7:719-742. [PMID: 37380750 PMCID: PMC10632090 DOI: 10.1038/s41551-023-01056-8] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 04/13/2023] [Indexed: 06/30/2023]
Abstract
In healthcare, the development and deployment of insufficiently fair systems of artificial intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models stratified across subpopulations have revealed inequalities in how patients are diagnosed, treated and billed. In this Perspective, we outline fairness in machine learning through the lens of healthcare, and discuss how algorithmic biases (in data acquisition, genetic variation and intra-observer labelling variability, in particular) arise in clinical workflows and the resulting healthcare disparities. We also review emerging technology for mitigating biases via disentanglement, federated learning and model explainability, and their role in the development of AI-based software as a medical device.
Collapse
Affiliation(s)
- Richard J Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Judy J Wang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Boston University School of Medicine, Boston, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tiffany Y Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jana Lipkova
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ming Y Lu
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sharifa Sahai
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Cancer Program, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA.
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Harvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
| |
Collapse
|
15
|
Bibb A, Schmidt K, Brink L, Pisano E, Coombs L, Apgar C, Dreyer K, Wald C. Specialty Society Support for Multicenter Research in Artificial Intelligence. Acad Radiol 2023; 30:640-643. [PMID: 36813668 DOI: 10.1016/j.acra.2023.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 01/06/2023] [Accepted: 01/08/2023] [Indexed: 02/22/2023]
Affiliation(s)
- Allen Bibb
- Grandview Medical Center, ACR Data Science Institute, Birmingham, Alabama.
| | | | - Laura Brink
- American College of Radiology, Reston, Virginia
| | - E Pisano
- American College of Radiology, Reston, Virginia
| | | | | | - Keith Dreyer
- Massachusetts General Hospital, ACR Data Science Institute, Boston, Massachusetts
| | - Christoph Wald
- Lahey Hospital and Medical Center, ACR Commission on Informatics, Boston, Massachusetts
| |
Collapse
|
16
|
|
17
|
Federated machine learning in data-protection-compliant research. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-022-00601-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
|
18
|
Oh W, Nadkarni GN. Federated Learning in Health care Using Structured Medical Data. ADVANCES IN KIDNEY DISEASE AND HEALTH 2023; 30:4-16. [PMID: 36723280 DOI: 10.1053/j.akdh.2022.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way to access a vast amount of data, sharing data outside the institutes involves legal, business, and technical challenges. Federated learning (FL) is a newly proposed machine learning framework for multicenter studies, tackling data-sharing issues across participant institutes. The promise of FL is simple. FL facilitates multicenter studies without losing data access control and allows the construction of a global model by aggregating local models trained from participant institutes. This article reviewed recently published studies that utilized FL in clinical studies with structured medical data. In addition, challenges and open questions in FL in clinical studies with structured medical data were discussed.
Collapse
Affiliation(s)
- Wonsuk Oh
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
| |
Collapse
|
19
|
Ogier du Terrail J, Leopold A, Joly C, Béguier C, Andreux M, Maussion C, Schmauch B, Tramel EW, Bendjebbar E, Zaslavskiy M, Wainrib G, Milder M, Gervasoni J, Guerin J, Durand T, Livartowski A, Moutet K, Gautier C, Djafar I, Moisson AL, Marini C, Galtier M, Balazard F, Dubois R, Moreira J, Simon A, Drubay D, Lacroix-Triki M, Franchet C, Bataillon G, Heudel PE. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat Med 2023; 29:135-146. [PMID: 36658418 DOI: 10.1038/s41591-022-02155-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 11/23/2022] [Indexed: 01/21/2023]
Abstract
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Camille Franchet
- Institut Universitaire du Cancer de Toulouse (IUCT) Oncopole, Toulouse, France
| | | | | |
Collapse
|
20
|
Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit. COMMUNICATIONS MEDICINE 2022; 2:162. [PMID: 36543940 PMCID: PMC9768782 DOI: 10.1038/s43856-022-00225-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Despite apparent promise and the availability of numerous examples in the literature, machine learning models are rarely used in practice in ICU units. This mismatch suggests that there are poorly understood barriers preventing uptake, which we aim to identify. METHODS We begin with a qualitative study with 29 interviews of 40 Intensive Care Unit-, hospital- and MedTech company staff members. As a follow-up to the study, we attempt to quantify some of the technical issues raised. To perform experiments we selected two models based on criteria such as medical relevance. Using these models we measure the loss of performance in predictive models due to drift over time, change of available patient features, scarceness of data, and deploying a model in a different context to the one it was built in. RESULTS The qualitative study confirms our assumptions on the potential of AI-driven analytics for patient care, as well as showing the prevalence and type of technical blocking factors that are responsible for its slow uptake. The experiments confirm that each of these issues can cause important loss of predictive model performance, depending on the model and the issue. CONCLUSIONS Based on the qualitative study and quantitative experiments we conclude that more research on practical solutions to enable AI-driven innovation in Intensive Care Units is needed. Furthermore, the general poor situation with respect to public, usable implementations of predictive models would appear to limit the possibilities for both the scientific repeatability of the underlying research and the transfer of this research into practice.
Collapse
|
21
|
Nguyen TX, Ran AR, Hu X, Yang D, Jiang M, Dou Q, Cheung CY. Federated Learning in Ocular Imaging: Current Progress and Future Direction. Diagnostics (Basel) 2022; 12:2835. [PMID: 36428895 PMCID: PMC9689273 DOI: 10.3390/diagnostics12112835] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
Abstract
Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a "centralised location". However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.
Collapse
Affiliation(s)
- Truong X. Nguyen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - An Ran Ran
- 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
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Meirui Jiang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Qi Dou
- Department of Computer Science and Engineering, 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
|
22
|
Teo ZL, Lee AY, Campbell P, Chan RVP, Ting DSW. Developments in Artificial Intelligence for Ophthalmology: Federated Learning. Asia Pac J Ophthalmol (Phila) 2022; 11:500-502. [PMID: 36417673 DOI: 10.1097/apo.0000000000000582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/04/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Zhen Ling Teo
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, US Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA
| | - Peter Campbell
- Department of Ophthalmology, Oregon Health and Science University, Portland, OR
| | - R V Paul Chan
- Department of Ophthalmology, University of Illinois Chicago, Chicago, IL
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore
- Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, Singapore
| |
Collapse
|
23
|
Brink L, Coombs LP, Kattil Veettil D, Kuchipudi K, Marella S, Schmidt K, Nair SS, Tilkin M, Treml C, Chang K, Kalpathy-Cramer J. ACR’s Connect and AI-LAB technical framework. JAMIA Open 2022; 5:ooac094. [PMID: 36380846 PMCID: PMC9651971 DOI: 10.1093/jamiaopen/ooac094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions. Materials and Methods Among its core capabilities, ACR Connect provides educational resources; tools for dataset annotation; model building and evaluation; and an interface for collaboration and federated learning across institutions without the need to move data off hospital premises. Results The AI-LAB application within ACR Connect allows users to investigate AI models using their own local data while maintaining data security. The software enables non-technical users to participate in the evaluation and training of AI models as part of a larger, collaborative network. Discussion Advancements in AI have transformed automated quantitative analysis for medical imaging. Despite the significant progress in research, AI is currently underutilized in current clinical workflows. The success of AI model development depends critically on the synergy between physicians who can drive clinical direction, data scientists who can design effective algorithms, and the availability of high-quality datasets. ACR Connect and AI-LAB provide a way to perform external validation as well as collaborative, distributed training. Conclusion In order to create a collaborative AI ecosystem across clinical and technical domains, the ACR developed a platform that enables non-technical users to participate in education and model development.
Collapse
Affiliation(s)
- Laura Brink
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Laura P Coombs
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Deepak Kattil Veettil
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Kashyap Kuchipudi
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Sailaja Marella
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Kendall Schmidt
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Sujith Surendran Nair
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Michael Tilkin
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Christopher Treml
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Ken Chang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Boston, Massachusetts, USA
- Department of Ophthalmology, University of Colorado School of Medicine , Aurora, Colorado, USA
| |
Collapse
|
24
|
Seastedt KP, Schwab P, O’Brien Z, Wakida E, Herrera K, Marcelo PGF, Agha-Mir-Salim L, Frigola XB, Ndulue EB, Marcelo A, Celi LA. Global healthcare fairness: We should be sharing more, not less, data. PLOS DIGITAL HEALTH 2022; 1:e0000102. [PMID: 36812599 PMCID: PMC9931202 DOI: 10.1371/journal.pdig.0000102] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2023]
Abstract
The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost-measured in terms of access to future medical innovations and clinical software-of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence's progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur-for the benefit of a global medical knowledge system.
Collapse
Affiliation(s)
- Kenneth P. Seastedt
- Beth Israel Deaconess Medical Center, Department of Surgery, Harvard Medical School, Boston, Massachusetts, United States of America
- * E-mail:
| | - Patrick Schwab
- GlaxoSmithKline, Artificial Intelligence & Machine Learning, Zug, Switzerland
| | - Zach O’Brien
- Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Edith Wakida
- Mbarara University of Science and Technology, Mbarara, Uganda
| | - Karen Herrera
- Quality and Patient Safety, Hospital Militar, Managua, Nicaragua
| | - Portia Grace F. Marcelo
- Department of Family & Community Medicine, University of the Philippines, Manila, Philippines
| | - Louis Agha-Mir-Salim
- Institute of Medical Informatics, Charité—Universitätsmedizin Berlin (corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health), Berlin, Germany
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, United States of America
| | - Xavier Borrat Frigola
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, United States of America
- Anesthesiology and Critical Care Department, Hospital Clinic de Barcelona, Barcelona, Spain
| | - Emily Boardman Ndulue
- Department of Journalism, Northeastern University, Boston, Massachusetts, United States of America
| | - Alvin Marcelo
- Department of Surgery, University of the Philippines, Manila, Philippines
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences & Technology, Cambridge, Massachusetts, United States of America
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Biostatistics Harvard T.H, Chan School of Public Health, Boston, Massachusetts, United States of America
| |
Collapse
|
25
|
Kumar S, Lakshminarayanan A, Chang K, Guretno F, Mien IH, Kalpathy-Cramer J, Krishnaswamy P, Singh P. Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling. DISTRIBUTED, COLLABORATIVE, AND FEDERATED LEARNING, AND AFFORDABLE AI AND HEALTHCARE FOR RESOURCE DIVERSE GLOBAL HEALTH : THIRD MICCAI WORKSHOP, DECAF 2022 AND SECOND MICCAI WORKSHOP, FAIR 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SIN... 2022; 13573:119-129. [PMID: 36745141 PMCID: PMC9890952 DOI: 10.1007/978-3-031-18523-6_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally - some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especially when there are hundreds of contributors. Existing SV computation techniques use approximations. However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible. For such settings, we propose an efficient SV computation technique called SaFE (Shapley Value for Federated Learning using Ensembling). We empirically show that SaFE computes values that are close to exact SVs, and that it performs better than current SV approximations. This is particularly relevant in medical imaging setting where widespread heterogeneity across institutions is rampant and fast accurate data valuation is required to determine the contribution of each participant in multi-institutional collaborative learning.
Collapse
Affiliation(s)
- Sourav Kumar
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Ken Chang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Feri Guretno
- Institute for Infocomm Research, ASTAR, Singapore
| | - Ivan Ho Mien
- Institute for Infocomm Research, ASTAR, Singapore
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Praveer Singh
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
26
|
Federated Learning in Ophthalmology: Retinopathy of Prematurity. Ophthalmol Retina 2022; 6:647-649. [PMID: 35933119 DOI: 10.1016/j.oret.2022.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 11/21/2022]
|
27
|
Ali Meerza SI, Li Z, Liu L, Zhang J, Liu J. Fair and Privacy-Preserving Alzheimer's Disease Diagnosis Based on Spontaneous Speech Analysis via Federated Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1362-1365. [PMID: 36086432 DOI: 10.1109/embc48229.2022.9871204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
As the most common neurodegenerative disease among older adults, Alzheimer's disease (AD) would lead to loss of memory, impaired language and judgment, gait disorders, and other cognitive deficits severe enough to interfere with daily activities and significantly diminish quality of life. Recent research has shown promising results in automatic AD diagnosis via speech, leveraging the advances of deep learning in the audio domain. However, most existing studies rely on a centralized learning framework which requires subjects' voice data to be gathered to a central server, raising severe privacy concerns. To resolve this, in this paper, we propose the first federated-learning-based approach for achieving automatic AD diagnosis via spontaneous speech analysis while ensuring the subjects' data privacy. Extensive experiments under various federated learning settings on the ADReSS challenge dataset show that the proposed model can achieve high accuracy for AD detection while achieving privacy preservation. To ensure fairness of the model performance across clients in federated settings, we further deploy fair aggregation mechanisms, particularly q-FEDAvg and q-FEDSgd, which greatly reduces the algorithmic biases due to the data heterogeneity among the clients. Clinical Relevance -The experiments were conducted on publicly available clinical datasets. No humans or animals were involved.
Collapse
|
28
|
Cui Y, Li Z, Liu L, Zhang J, Liu J. Privacy-preserving Speech-based Depression Diagnosis via Federated Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1371-1374. [PMID: 36085955 DOI: 10.1109/embc48229.2022.9871861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mental health disorders, such as depression, affect a large and growing number of populations worldwide, and they may cause severe emotional, behavioral and physical health problems if left untreated. As depression affects a patient's speech characteristics, recent studies have proposed to leverage deep-learning-powered speech analysis models for depression diagnosis, which often require centralized learning on the collected voice data. However, this centralized training requiring data to be stored at a server raises the risks of severe voice data breaches, and people may not be willing to share their speech data with third parties due to privacy concerns. To address these issues, in this paper, we demonstrate for the first time that speech-based depression diagnosis models can be trained in a privacy-preserving way using federated learning, which enables collaborative model training while keeping the private speech data decentralized on clients' devices. To ensure the model's robustness under attacks, we also integrate different FL defenses into the system, such as norm bounding, differential privacy, and secure aggregation mechanisms. Extensive experiments under various FL settings on the DAIC-WOZ dataset show that our FL model can achieve high performance without sacrificing much utility compared with centralized-learning approaches while ensuring users' speech data privacy. Clinical Relevance- The experiments were conducted on publicly available clinical datasets. No humans or animals were involved.
Collapse
|
29
|
Privacy-preserving federated neural network learning for disease-associated cell classification. PATTERNS 2022; 3:100487. [PMID: 35607628 PMCID: PMC9122966 DOI: 10.1016/j.patter.2022.100487] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/14/2022] [Accepted: 03/14/2022] [Indexed: 11/21/2022]
Abstract
Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach, PriCell, for complex models such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption and enables the collaborative training of encrypted neural networks with multiple healthcare institutions. We preserve the confidentiality of each institutions’ input data, of any intermediate values, and of the trained model parameters. We efficiently replicate the training of a published state-of-the-art convolutional neural network architecture in a decentralized and privacy-preserving manner. Our solution achieves an accuracy comparable with the one obtained with the centralized non-secure solution. PriCell guarantees patient privacy and ensures data utility for efficient multi-center studies involving complex healthcare data. We enable collaborative and privacy-preserving model training between institutions Training under encryption does not degrade the utility of the data We apply our solution to the single-cell analysis in a federated setting Our method is generalizable to other machine learning tasks in the healthcare domain
High-quality medical machine learning models will benefit greatly from collaboration between health care institutions. Yet, it is usually difficult to transfer data between these institutions due to strict privacy regulations. In this study, we propose a solution, PriCell, that relies on multiparty homomorphic encryption to enable privacy-preserving collaborative machine learning while protecting via encryption the institutions' input data, the model, and any value exchanged between the institutions. We show the maturity of our solution by training a published state-of-the-art convolutional neural network in a decentralized and privacy-preserving manner. We compare the accuracy achieved by PriCell with the centralized and non-secure solutions and show that PriCell guarantees privacy without reducing the utility of the data. The benefits of PriCell constitute an important landmark for real-world applications of collaborative training while preserving privacy.
Collapse
|
30
|
Ge F, Qian H, Lei J, Ni Y, Li Q, Wang S, Ding K. Experiences and Challenges of Emerging Online Health Services Combating COVID-19 in China: A Retrospective, Cross-sectional Study of Internet Hospitals. JMIR Med Inform 2022; 10:e37042. [PMID: 35500013 PMCID: PMC9162135 DOI: 10.2196/37042] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 03/21/2022] [Accepted: 04/28/2022] [Indexed: 01/17/2023] Open
Abstract
Background Internet-based online virtual health services were originally an important way for the Chinese government to resolve unmet medical service needs due to inadequate medical institutions. Its initial development was not well received. Then, the unexpected COVID-19 pandemic produced a tremendous demand for telehealth in a short time, which stimulated the explosive development of internet hospitals. The Second Affiliated Hospital of Zhejiang University (SAHZU) has taken a leading role in the construction of internet hospitals in China. The pandemic triggered the hospital to develop unique research on health service capacity under strict quarantine policies and to predict long-term trends. Objective This study aims to provide policy enlightenment for the construction of internet-based health services to better fight against COVID-19 and to elucidate future directions through an in-depth analysis of 2 years of online health service data gleaned from SAHZU’s experiences and lessons learned. Methods We collected data from SAHZU Internet Hospital from November 1, 2019, to September 16, 2021. Data from over 900,000 users were analyzed with respect to demographic characteristics, demands placed on departments by user needs, new registrations, and consultation behaviors. Interrupted time series (ITS) analysis was adopted to evaluate the impact of this momentous emergency event and its long-term trends. With theme analysis and a defined 2D model, 3 investigations were conducted synchronously to determine users’ authentic demands on online hospitals. Results The general profile of internet hospital users is young or middle-aged women who live in Zhejiang and surrounding provinces. The ITS model indicated that, after the intervention (the strict quarantine policies) was implemented during the outbreak, the number of internet hospital users significantly increased (β_2=105.736, P<.001). Further, long-term waves of COVID-19 led to an increasing number of users following the outbreak (β_3=0.167, P<.001). In theme analysis, we summarized 8 major demands by users of the SAHZU internet hospital during the national shutdown period and afterwards. Online consultations and information services were persistent and universal demands, followed by concerns about medical safety and quality, time, and cost. Users’ medical behavior patterns changed from onsite to online as internet hospital demands increased. Conclusions The pandemic has spawned the explosive growth of telehealth; as a public tertiary internet hospital, the SAHZU internet hospital is partially and irreversibly integrated into the traditional medical system. As we shared the practical examples of 1 public internet hospital in China, we put forward suggestions about the future direction of telehealth. Vital experience in the construction of internet hospitals was provided in the normalization of COVID-19 prevention and control, which can be demonstrated as a model of internet hospital management practice for other medical institutions.
Collapse
Affiliation(s)
- Fangmin Ge
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, CN
| | - Huan Qian
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, CN
| | - Jianbo Lei
- Center for Medical Informatics, Health Science Center, Peking University, Beijing, CN
| | - Yiqi Ni
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, CN
| | - Qian Li
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, CN
| | - Song Wang
- School of Management, Zhejiang University, Hangzhou, CN
| | - Kefeng Ding
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, 88 Jiefang Road, Hangzhou, CN
| |
Collapse
|
31
|
Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, Boeker M, Obid P, Lang GM. Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models. J Pers Med 2022; 12:jpm12040509. [PMID: 35455625 PMCID: PMC9029065 DOI: 10.3390/jpm12040509] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/18/2022] [Accepted: 03/19/2022] [Indexed: 12/22/2022] Open
Abstract
Healthcare systems worldwide generate vast amounts of data from many different sources. Although of high complexity for a human being, it is essential to determine the patterns and minor variations in the genomic, radiological, laboratory, or clinical data that reliably differentiate phenotypes or allow high predictive accuracy in health-related tasks. Convolutional neural networks (CNN) are increasingly applied to image data for various tasks. Its use for non-imaging data becomes feasible through different modern machine learning techniques, converting non-imaging data into images before inputting them into the CNN model. Considering also that healthcare providers do not solely use one data modality for their decisions, this approach opens the door for multi-input/mixed data models which use a combination of patient information, such as genomic, radiological, and clinical data, to train a hybrid deep learning model. Thus, this reflects the main characteristic of artificial intelligence: simulating natural human behavior. The present review focuses on key advances in machine and deep learning, allowing for multi-perspective pattern recognition across the entire information set of patients in spine surgery. This is the first review of artificial intelligence focusing on hybrid models for deep learning applications in spine surgery, to the best of our knowledge. This is especially interesting as future tools are unlikely to use solely one data modality. The techniques discussed could become important in establishing a new approach to decision-making in spine surgery based on three fundamental pillars: (1) patient-specific, (2) artificial intelligence-driven, (3) integrating multimodal data. The findings reveal promising research that already took place to develop multi-input mixed-data hybrid decision-supporting models. Their implementation in spine surgery may hence be only a matter of time.
Collapse
Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Correspondence:
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, 79100 Freiburg, Germany; (F.H.); (A.Z.)
| | - Veronika Shavlokhova
- Department of Oral and Maxillofacial Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, 5020 Salzburg, Austria;
- Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
| | - Martin Boeker
- Intelligence and Informatics in Medicine, Medical Center Rechts der Isar, School of Medicine, Technical University of Munich, 81675 Munich, Germany;
| | - Peter Obid
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
| | - Gernot Michael Lang
- Department of Orthopedics and Trauma Surgery, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79108 Freiburg, Germany; (S.Ü.); (P.O.); (G.M.L.)
| |
Collapse
|
32
|
Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations. Drug Saf 2022; 45:493-510. [PMID: 35579813 PMCID: PMC9112258 DOI: 10.1007/s40264-022-01158-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2022] [Indexed: 01/28/2023]
Abstract
Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. The traditional practice of having numerous groups use single databases to accomplish similar tasks and address common questions about medical products can be made more efficient through well-coordinated multi-database studies, greatly facilitated through distributed data network (DDN) architectures. Access to larger amounts of electronic health data within DDNs has created a growing interest in using data-adaptive machine learning (ML) techniques that can automatically model complex associations in high-dimensional data with minimal human guidance. However, the siloed storage and diverse nature of the databases in DDNs create unique challenges for using ML. In this paper, we discuss opportunities, challenges, and considerations for applying ML in DDNs for pharmacoepidemiologic and pharmacovigilance studies. We first discuss major types of activities performed by DDNs and how ML may be used. Next, we discuss practical data-related factors influencing how DDNs work in practice. We then combine these discussions and jointly consider how opportunities for ML are affected by practical data-related factors for DDNs, leading to several challenges. We present different approaches for addressing these challenges and highlight efforts that real-world DDNs have taken or are currently taking to help mitigate them. Despite these challenges, the time is ripe for the emerging interest to use ML in DDNs, and the utility of these data-adaptive modeling techniques in pharmacoepidemiologic and pharmacovigilance studies will likely continue to increase in the coming years.
Collapse
|
33
|
Sezgin E, Sirrianni J, Linwood SL. Operationalizing and implementing pretrained large AI linguistic models in the United States healthcare system: An outlook of GPT-3 as a service (Preprint). JMIR Med Inform 2021; 10:e32875. [PMID: 35142635 PMCID: PMC8874824 DOI: 10.2196/32875] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 12/14/2021] [Accepted: 01/09/2022] [Indexed: 11/15/2022] Open
Abstract
Generative pretrained transformer models have been popular recently due to their enhanced capabilities and performance. In contrast to many existing artificial intelligence models, generative pretrained transformer models can perform with very limited training data. Generative pretrained transformer 3 (GPT-3) is one of the latest releases in this pipeline, demonstrating human-like logical and intellectual responses to prompts. Some examples include writing essays, answering complex questions, matching pronouns to their nouns, and conducting sentiment analyses. However, questions remain with regard to its implementation in health care, specifically in terms of operationalization and its use in clinical practice and research. In this viewpoint paper, we briefly introduce GPT-3 and its capabilities and outline considerations for its implementation and operationalization in clinical practice through a use case. The implementation considerations include (1) processing needs and information systems infrastructure, (2) operating costs, (3) model biases, and (4) evaluation metrics. In addition, we outline the following three major operational factors that drive the adoption of GPT-3 in the US health care system: (1) ensuring Health Insurance Portability and Accountability Act compliance, (2) building trust with health care providers, and (3) establishing broader access to the GPT-3 tools. This viewpoint can inform health care practitioners, developers, clinicians, and decision makers toward understanding the use of the powerful artificial intelligence tools integrated into hospital systems and health care.
Collapse
Affiliation(s)
- Emre Sezgin
- The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Joseph Sirrianni
- The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, United States
| | - Simon L Linwood
- School of Medicine, University of California Riverside, Riverside, CA, United States
| |
Collapse
|