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Żydowicz WM, Skokowski J, Marano L, Polom K. Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients. J Clin Med 2024; 13:4337. [PMID: 39124604 PMCID: PMC11313674 DOI: 10.3390/jcm13154337] [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: 04/09/2024] [Revised: 05/22/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.
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Affiliation(s)
- Weronika Magdalena Żydowicz
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
| | - Jaroslaw Skokowski
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Luigi Marano
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Karol Polom
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznan, Poland
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Pati S, Kumar S, Varma A, Edwards B, Lu C, Qu L, Wang JJ, Lakshminarayanan A, Wang SH, Sheller MJ, Chang K, Singh P, Rubin DL, Kalpathy-Cramer J, Bakas S. Privacy preservation for federated learning in health care. PATTERNS (NEW YORK, N.Y.) 2024; 5:100974. [PMID: 39081567 PMCID: PMC11284498 DOI: 10.1016/j.patter.2024.100974] [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] [Indexed: 08/02/2024]
Abstract
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.
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Affiliation(s)
- Sarthak Pati
- Center for Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sourav Kumar
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Amokh Varma
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Charles Lu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Center for Clinical Data Science, Massachusetts General Hospital and Brigham and Women’s Hospital, Boston, MA, USA
| | - Liangqiong Qu
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China
| | - Justin J. Wang
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA
| | | | | | | | - Ken Chang
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Praveer Singh
- University of Colorado School of Medicine, Aurora, CO, USA
| | - Daniel L. Rubin
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA
| | | | - Spyridon Bakas
- Center for Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN, USA
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Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel) 2024; 14:1456. [PMID: 39001346 PMCID: PMC11240935 DOI: 10.3390/diagnostics14131456] [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: 05/30/2024] [Revised: 07/01/2024] [Accepted: 07/06/2024] [Indexed: 07/16/2024] Open
Abstract
The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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Affiliation(s)
- Jin Y Chang
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | - Mina S Makary
- Department of Radiology, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Yuan S, Xu S, Lu X, Chen X, Wang Y, Bao R, Sun Y, Xiao X, Su L, Long Y, Li L, He H. A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia. Sci Rep 2024; 14:15589. [PMID: 38971879 PMCID: PMC11227531 DOI: 10.1038/s41598-024-66596-8] [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: 01/05/2024] [Accepted: 07/02/2024] [Indexed: 07/08/2024] Open
Abstract
Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and treatment. The goal of this study is to develop a privacy-preserving federated learning framework for predicting candidemia in ICU patients. This approach aims to enhance the accuracy of antifungal drug prescriptions and patient outcomes. This study involved the creation of four predictive FL models for candidemia using data from ICU patients across three hospitals in China. The models were designed to prioritize patient privacy while aggregating learnings across different sites. A unique ensemble feature selection strategy was implemented, combining the strengths of XGBoost's feature importance and statistical test p values. This strategy aimed to optimize the selection of relevant features for accurate predictions. The federated learning models demonstrated significant improvements over locally trained models, with a 9% increase in the area under the curve (AUC) and a 24% rise in true positive ratio (TPR). Notably, the FL models excelled in the combined TPR + TNR metric, which is critical for feature selection in candidemia prediction. The ensemble feature selection method proved more efficient than previous approaches, achieving comparable performance. The study successfully developed a set of federated learning models that significantly enhance the prediction of candidemia in ICU patients. By leveraging a novel feature selection method and maintaining patient privacy, the models provide a robust framework for improved clinical decision-making in the treatment of candidemia.
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Affiliation(s)
- Siyi Yuan
- Peking Union Medical College Hospital (CAMS), Beijing, China
| | - Song Xu
- Yidu Cloud Technology Company Ltd., Beijing, China
| | - Xiao Lu
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiangyu Chen
- Peking Union Medical College Hospital (CAMS), Beijing, China
- Peking Union Medical College Graduate School, Beijing, China
| | - Yao Wang
- Yidu Cloud Technology Company Ltd., Beijing, China
| | - Renyi Bao
- Yidu Cloud Technology Company Ltd., Beijing, China
| | - Yunbo Sun
- Department of Intensive Care Unit, Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiongjian Xiao
- Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Longxiang Su
- Peking Union Medical College Hospital (CAMS), Beijing, China
| | - Yun Long
- Peking Union Medical College Hospital (CAMS), Beijing, China.
| | - Linfeng Li
- Yidu Cloud Technology Company Ltd., Beijing, China.
| | - Huaiwu He
- Peking Union Medical College Hospital (CAMS), Beijing, China.
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Shiri I, Salimi Y, Sirjani N, Razeghi B, Bagherieh S, Pakbin M, Mansouri Z, Hajianfar G, Avval AH, Askari D, Ghasemian M, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khosravi B, Bijari S, Sayfollahi S, Atashzar MR, Hasanian M, Shahhamzeh A, Teimouri A, Goharpey N, Shirzad-Aski H, Karimi J, Radmard AR, Rezaei-Kalantari K, Oghli MG, Oveisi M, Vafaei Sadr A, Voloshynovskiy S, Zaidi H. Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset. Med Phys 2024; 51:4736-4747. [PMID: 38335175 DOI: 10.1002/mp.16964] [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: 10/05/2023] [Revised: 01/10/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model. PURPOSE This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images. METHODS After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. RESULTS The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. CONCLUSION The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.
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Affiliation(s)
- Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Yazdan Salimi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Nasim Sirjani
- Research and Development Department, Med Fanavarn Plus Co, Karaj, Iran
| | - Behrooz Razeghi
- Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Sara Bagherieh
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Masoumeh Pakbin
- Imaging Department, Qom University of Medical Sciences, Qom, Iran
| | - Zahra Mansouri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | - Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
| | | | - Dariush Askari
- Department of Radiology Technology, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Ghasemian
- Department of Radiology, Shahid Beheshti Hospital, Qom University of Medical Sciences, Qom, Iran
| | - Saleh Sandoughdaran
- Department of Clinical Oncology, Royal Surrey County Hospital, Guildford, UK
| | - Ahmad Sohrabi
- Radin Makian Azma Mehr Ltd., Radinmehr Veterinary Laboratory, Iran University of Medical Sciences, Gorgan, Iran
| | - Elham Sadati
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Somayeh Livani
- Clinical Research Development Unit (CRDU), Sayad Shirazi Hospital, Golestan University of Medical Sciences, Gorgan, Iran
| | - Pooya Iranpour
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Bardia Khosravi
- Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Salar Bijari
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Sahar Sayfollahi
- Department of Neurosurgery, Faculty of Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Atashzar
- Department of Immunology, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Mohammad Hasanian
- Department of Radiology, Arak University of Medical Sciences, Arak, Iran
| | - Alireza Shahhamzeh
- Clinical research development center, Qom University of Medical Sciences, Qom, Iran
| | - Arash Teimouri
- Medical Imaging Research Center, Department of Radiology, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Neda Goharpey
- Department of radiation oncology, Shohada-e Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Jalal Karimi
- Department of Infectious Disease, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Amir Reza Radmard
- Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Kiara Rezaei-Kalantari
- Rajaie Cardiovascular, Medical & Research Center, Iran University of Medical Science, Tehran, Iran
| | | | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alireza Vafaei Sadr
- Department of Public Health Sciences, College of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA
| | | | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
- University Research and Innovation Center, Óbuda University, Budapest, Hungary
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Guan H, Yap PT, Bozoki A, Liu M. Federated learning for medical image analysis: A survey. PATTERN RECOGNITION 2024; 151:110424. [PMID: 38559674 PMCID: PMC10976951 DOI: 10.1016/j.patcog.2024.110424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.
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Affiliation(s)
- Hao Guan
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Andrea Bozoki
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Shao J, Ma J, Yu Y, Zhang S, Wang W, Li W, Wang C. A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections. Innovation (N Y) 2024; 5:100648. [PMID: 39021525 PMCID: PMC11253137 DOI: 10.1016/j.xinn.2024.100648] [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: 09/25/2023] [Accepted: 05/19/2024] [Indexed: 07/20/2024] Open
Abstract
Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management. In this study, we successfully developed a multimodal integration (MMI) pipeline to differentiate among bacterial, fungal, and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients. The area under the curve (AUC) of the MMI system comprising clinical text and computed tomography (CT) image scans yielded 0.910 (95% confidence interval [CI]: 0.904-0.916) and 0.887 (95% CI: 0.867-0.909) in the internal and external testing datasets respectively, which were comparable to those of experienced physicians. Furthermore, the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822 (95% CI: 0.805-0.837) and bacterial subtypes with a mean AUC of 0.803 (95% CI: 0.775-0.830). Here, the MMI system harbors the potential to guide tailored medication recommendations, thus mitigating the risk of antibiotic misuse. Additionally, the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness, contributing to more informed clinical decision-making. To revolutionize medical care, embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.
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Affiliation(s)
- Jun Shao
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu 610213, China
| | - Jiechao Ma
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Hong Kong SAR, China
| | - Shu Zhang
- AI Lab, Deepwise Healthcare, Beijing 100080, China
| | - Wenyang Wang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
| | - Weimin Li
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu 610213, China
| | - Chengdi Wang
- Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, West China School of Medicine, Sichuan University, Chengdu 610041, China
- Targeted Tracer Research and Development Laboratory, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610213, China
- Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu 610213, China
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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.
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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.
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Schmidt K, Bearce B, Chang K, Coombs L, Farahani K, Elbatel M, Mouheb K, Marti R, Zhang R, Zhang Y, Wang Y, Hu Y, Ying H, Xu Y, Testagrose C, Demirer M, Gupta V, Akünal Ü, Bujotzek M, Maier-Hein KH, Qin Y, Li X, Kalpathy-Cramer J, Roth HR. Fair evaluation of federated learning algorithms for automated breast density classification: The results of the 2022 ACR-NCI-NVIDIA federated learning challenge. Med Image Anal 2024; 95:103206. [PMID: 38776844 DOI: 10.1016/j.media.2024.103206] [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: 06/22/2023] [Revised: 02/15/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
The correct interpretation of breast density is important in the assessment of breast cancer risk. AI has been shown capable of accurately predicting breast density, however, due to the differences in imaging characteristics across mammography systems, models built using data from one system do not generalize well to other systems. Though federated learning (FL) has emerged as a way to improve the generalizability of AI without the need to share data, the best way to preserve features from all training data during FL is an active area of research. To explore FL methodology, the breast density classification FL challenge was hosted in partnership with the American College of Radiology, Harvard Medical Schools' Mass General Brigham, University of Colorado, NVIDIA, and the National Institutes of Health National Cancer Institute. Challenge participants were able to submit docker containers capable of implementing FL on three simulated medical facilities, each containing a unique large mammography dataset. The breast density FL challenge ran from June 15 to September 5, 2022, attracting seven finalists from around the world. The winning FL submission reached a linear kappa score of 0.653 on the challenge test data and 0.413 on an external testing dataset, scoring comparably to a model trained on the same data in a central location.
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Affiliation(s)
| | - Benjamin Bearce
- The Massachusetts General Hospital, USA; University of Colorado, USA
| | - Ken Chang
- The Massachusetts General Hospital, USA
| | | | - Keyvan Farahani
- National Institutes of Health National Cancer Institute, USA
| | - Marawan Elbatel
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Kaouther Mouheb
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Robert Marti
- Computer Vision and Robotics Institute, University of Girona, Spain
| | - Ruipeng Zhang
- Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, China; Shanghai AI Laboratory, China
| | | | - Yanfeng Wang
- Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, China; Shanghai AI Laboratory, China
| | - Yaojun Hu
- Real Doctor AI Research Centre, Zhejiang University, China
| | - Haochao Ying
- Real Doctor AI Research Centre, Zhejiang University, China; School of Public Health, Zhejiang University, China
| | - Yuyang Xu
- Real Doctor AI Research Centre, Zhejiang University, China; College of Computer Science and Technology, Zhejiang University, China
| | | | | | | | - Ünal Akünal
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Markus Bujotzek
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Klaus H Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Yi Qin
- Electronic and Computer Engineering, Hong Kong University of Science and Technology, China
| | - Xiaomeng Li
- Electronic and Computer Engineering, Hong Kong University of Science and Technology, China
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10
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Kumar R, Bernard CM, Ullah A, Khan RU, Kumar J, Kulevome DKB, Yunbo R, Zeng S. Privacy-preserving blockchain-based federated learning for brain tumor segmentation. Comput Biol Med 2024; 177:108646. [PMID: 38824788 DOI: 10.1016/j.compbiomed.2024.108646] [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: 08/30/2023] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/04/2024]
Abstract
Improved data sharing between healthcare providers can lead to a higher probability of accurate diagnosis, more effective treatments, and enhanced capabilities of healthcare organizations. One critical area of focus is brain tumor segmentation, a complex task due to the heterogeneous appearance, irregular shape, and variable location of tumors. Accurate segmentation is essential for proper diagnosis and effective treatment planning, yet current techniques often fall short due to these complexities. However, the sensitive nature of health data often prohibits its sharing. Moreover, the healthcare industry faces significant issues, including preserving the privacy of the model and instilling trust in the model. This paper proposes a framework to address these privacy and trust issues by introducing a mechanism for training the global model using federated learning and sharing the encrypted learned parameters via a permissioned blockchain. The blockchain-federated learning algorithm we designed aggregates gradients in the permissioned blockchain to decentralize the global model, while the introduced masking approach retains the privacy of the model parameters. Unlike traditional raw data sharing, this approach enables hospitals or medical research centers to contribute to a globally learned model, thereby enhancing the performance of the central model for all participating medical entities. As a result, the global model can learn about several specific diseases and benefit each contributor with new disease diagnosis tasks, leading to improved treatment options. The proposed algorithm ensures the quality of model data when aggregating the local model, using an asynchronous federated learning procedure to evaluate the shared model's quality. The experimental results demonstrate the efficacy of the proposed scheme for the critical and challenging task of brain tumor segmentation. Specifically, our method achieved a 1.99% improvement in Dice similarity coefficient for enhancing tumors and a 19.08% reduction in Hausdorff distance for whole tumors compared to the baseline methods, highlighting the significant advancement in segmentation performance and reliability.
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Affiliation(s)
- Rajesh Kumar
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
| | - Cobbinah M Bernard
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China.
| | - Aman Ullah
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
| | - Riaz Ullah Khan
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
| | - Jay Kumar
- Institute for Big Data Analytics, Dalhousie University, Halifax, NS, Canada
| | - Delanyo K B Kulevome
- Department of Intelligent Manufacturing and Control Engineering, Qilu Institute of Technology, Jinan, Shangdong, 250200, China
| | - Rao Yunbo
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
| | - Shaoning Zeng
- Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
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11
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Ahmed R, Maddikunta PKR, Gadekallu TR, Alshammari NK, Hendaoui FA. Efficient differential privacy enabled federated learning model for detecting COVID-19 disease using chest X-ray images. Front Med (Lausanne) 2024; 11:1409314. [PMID: 38912338 PMCID: PMC11193384 DOI: 10.3389/fmed.2024.1409314] [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: 03/29/2024] [Accepted: 05/15/2024] [Indexed: 06/25/2024] Open
Abstract
The rapid spread of COVID-19 pandemic across the world has not only disturbed the global economy but also raised the demand for accurate disease detection models. Although many studies have proposed effective solutions for the early detection and prediction of COVID-19 with Machine Learning (ML) and Deep learning (DL) based techniques, but these models remain vulnerable to data privacy and security breaches. To overcome the challenges of existing systems, we introduced Adaptive Differential Privacy-based Federated Learning (DPFL) model for predicting COVID-19 disease from chest X-ray images which introduces an innovative adaptive mechanism that dynamically adjusts privacy levels based on real-time data sensitivity analysis, improving the practical applicability of Federated Learning (FL) in diverse healthcare environments. We compared and analyzed the performance of this distributed learning model with a traditional centralized model. Moreover, we enhance the model by integrating a FL approach with an early stopping mechanism to achieve efficient COVID-19 prediction with minimal communication overhead. To ensure privacy without compromising model utility and accuracy, we evaluated the proposed model under various noise scales. Finally, we discussed strategies for increasing the model's accuracy while maintaining robustness as well as privacy.
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Affiliation(s)
- Rawia Ahmed
- Computer Science Department, Applied College, University of Ha’il, Ha’il, Saudi Arabia
| | - Praveen Kumar Reddy Maddikunta
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Thippa Reddy Gadekallu
- The College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China
- Division of Research and Development, Lovely Professional University, Phagwara, India
- Center of Research Impact and Outcome, Chitkara University, Rajpura, India
| | - Naif Khalaf Alshammari
- Mechanical Engineering Department, Engineering College, University of Ha’il, Ha’il, Saudi Arabia
| | - Fatma Ali Hendaoui
- Computer Science Department, Applied College, University of Ha’il, Ha’il, Saudi Arabia
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12
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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.
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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.
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13
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van Genderen ME, Cecconi M, Jung C. Federated data access and federated learning: improved data sharing, AI model development, and learning in intensive care. Intensive Care Med 2024; 50:974-977. [PMID: 38635044 PMCID: PMC11164808 DOI: 10.1007/s00134-024-07408-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 03/23/2024] [Indexed: 04/19/2024]
Affiliation(s)
- Michel E van Genderen
- Department of Adult Intensive Care, Erasmus MC, University Medical Center Rotterdam, (internal postadress-Room Ne-403), Doctor molewaterplein 40, 3015 GD, Rotterdam, The Netherlands.
| | - Maurizio Cecconi
- Biomedical Sciences Department, Humanitas University, Milan, Italy
- Department of Anaesthesia and Intensive Care, IRCCS Humanitas Research Hospital, Milan, Italy
| | - Christian Jung
- Medical Faculty, Department of Cardiology, Pulmonology and Vascular Medicine, Heinrich-Heine-University Duesseldorf, Duesseldorf, Germany
- Medical Faculty and University Hospital of Düsseldorf, Cardiovascular Research Institute Düsseldorf (CARID), Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany
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14
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Zhou J, Wang X, Li Y, Yang Y, Shi J. Federated-learning-based prognosis assessment model for acute pulmonary thromboembolism. BMC Med Inform Decis Mak 2024; 24:141. [PMID: 38802861 PMCID: PMC11131248 DOI: 10.1186/s12911-024-02543-x] [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: 01/23/2024] [Accepted: 05/17/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Acute pulmonary thromboembolism (PTE) is a common cardiovascular disease and recognizing low prognosis risk patients with PTE accurately is significant for clinical treatment. This study evaluated the value of federated learning (FL) technology in PTE prognosis risk assessment while ensuring the security of clinical data. METHODS A retrospective dataset consisted of PTE patients from 12 hospitals were collected, and 19 physical indicators of patients were included to train the FL-based prognosis assessment model to predict the 30-day death event. Firstly, multiple machine learning methods based on FL were compared to choose the superior model. And then performance of models trained on the independent (IID) and non-independent identical distributed(Non-IID) datasets was calculated and they were tested further on Real-world data. Besides, the optimal model was compared with pulmonary embolism severity index (PESI), simplified PESI (sPESI), Peking Union Medical College Hospital (PUMCH). RESULTS The area under the receiver operating characteristic curve (AUC) of logistic regression(0.842) outperformed convolutional neural network (0.819) and multi layer perceptron (0.784). Under IID, AUC of model trained using FL(Fed) on the training, validation and test sets was 0.852 ± 0.002, 0.867 ± 0.012 and 0.829 ± 0.004. Under Real-world, AUC of Fed was 0.855 ± 0.005, 0.882 ± 0.003 and 0.835 ± 0.005. Under IID and Real-world, AUC of Fed surpassed centralization model(NonFed) (0.847 ± 0.001, 0.841 ± 0.001 and 0.811 ± 0.001). Under Non-IID, although AUC of Fed (0.846 ± 0.047) outperformed NonFed (0.841 ± 0.001) on validation set, it (0.821 ± 0.016 and 0.799 ± 0.031) slightly lagged behind NonFed (0.847 ± 0.001 and 0.811 ± 0.001) on the training and test sets. In practice, AUC of Fed (0.853, 0.884 and 0.842) outshone PESI (0.812, 0.789 and 0.791), sPESI (0.817, 0.770 and 0.786) and PUMCH(0.848, 0.814 and 0.832) on the training, validation and test sets. Additionally, Fed (0.842) exhibited higher AUC values across test sets compared to those trained directly on the clients (0.758, 0.801, 0.783, 0.741, 0.788). CONCLUSIONS In this study, the FL based machine learning model demonstrated commendable efficacy on PTE prognostic risk prediction, rendering it well-suited for deployment in hospitals.
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Affiliation(s)
- Jun Zhou
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xin Wang
- Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China
| | - Yiyao Li
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China
| | - Yuqing Yang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Juhong Shi
- Department of Pulmonary and Critical Care Medicine, Peking Union Medical College Hospital, Beijing, China.
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15
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Babar M, Qureshi B, Koubaa A. Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging. PLoS One 2024; 19:e0302539. [PMID: 38748657 PMCID: PMC11095741 DOI: 10.1371/journal.pone.0302539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 04/09/2024] [Indexed: 05/19/2024] Open
Abstract
In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.
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Affiliation(s)
- Muhammad Babar
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
| | - Basit Qureshi
- College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - Anis Koubaa
- Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia
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16
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Li X, Jones P, Zhao M. Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning. Sci Rep 2024; 14:11022. [PMID: 38745042 PMCID: PMC11094099 DOI: 10.1038/s41598-024-61851-4] [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: 06/08/2023] [Accepted: 05/10/2024] [Indexed: 05/16/2024] Open
Abstract
The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.
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Affiliation(s)
- Xiaopeng Li
- Department of Neurology, The First Affiliated Hospital of Henan University, Kaifeng, China
| | - Peng Jones
- Independent Researcher, Xinyang, Henan, China
| | - Mei Zhao
- Department of Neurology, The First Affiliated Hospital of Nanchang University, No. 17 Yongwai Street, Nanchang, 330006, Jiangxi, China.
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17
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Wang H, Jing H, Yang J, Liu C, Hu L, Tao G, Zhao Z, Shen N. Identifying autism spectrum disorder from multi-modal data with privacy-preserving. NPJ MENTAL HEALTH RESEARCH 2024; 3:15. [PMID: 38698164 PMCID: PMC11066078 DOI: 10.1038/s44184-023-00050-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 12/20/2023] [Indexed: 05/05/2024]
Abstract
The application of deep learning models to precision medical diagnosis often requires the aggregation of large amounts of medical data to effectively train high-quality models. However, data privacy protection mechanisms make it difficult to perform medical data collection from different medical institutions. In autism spectrum disorder (ASD) diagnosis, automatic diagnosis using multimodal information from heterogeneous data has not yet achieved satisfactory performance. To address the privacy preservation issue as well as to improve ASD diagnosis, we propose a deep learning framework using multimodal feature fusion and hypergraph neural networks for disease prediction in federated learning (FedHNN). By introducing the federated learning strategy, each local model is trained and computed independently in a distributed manner without data sharing, allowing rapid scaling of medical datasets to achieve robust and scalable deep learning predictive models. To further improve the performance with privacy preservation, we improve the hypergraph model for multimodal fusion to make it suitable for autism spectrum disorder (ASD) diagnosis tasks by capturing the complementarity and correlation between modalities through a hypergraph fusion strategy. The results demonstrate that our proposed federated learning-based prediction model is superior to all local models and outperforms other deep learning models. Overall, our proposed FedHNN has good results in the work of using multi-site data to improve the performance of ASD identification.
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Affiliation(s)
- Haishuai Wang
- College of Computer Science, Zhejiang University, Hangzhou, China.
| | - Hezi Jing
- College of Computer Science, Tianjin Normal University, Tianjin, China
| | - Jianjun Yang
- Department of General Practice, Shandong Provincial Third Hospital, Shandong University, Jinan, China
| | - Chao Liu
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Liwei Hu
- Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiao Tong University, Shanghai, China
| | - Guangyu Tao
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ziping Zhao
- College of Computer Science, Tianjin Normal University, Tianjin, China.
| | - Ning Shen
- Liangzhu Laboratory, School of Medicine, Zhejiang University, Hangzhou, China.
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18
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Suresh V, Singh KK, Vaish E, Gurjar M, Ambuli Nambi A, Khulbe Y, Muzaffar S. Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus 2024; 16:e59797. [PMID: 38846182 PMCID: PMC11154024 DOI: 10.7759/cureus.59797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Artificial intelligence (AI) is a technique that attempts to replicate human intelligence, analytical behavior, and decision-making ability. This includes machine learning, which involves the use of algorithms and statistical techniques to enhance the computer's ability to make decisions more accurately. Due to AI's ability to analyze, comprehend, and interpret considerable volumes of data, it has been increasingly used in the field of healthcare. In critical care medicine, where most of the patient load requires timely interventions due to the perilous nature of the condition, AI's ability to monitor, analyze, and predict unfavorable outcomes is an invaluable asset. It can significantly improve timely interventions and prevent unfavorable outcomes, which, otherwise, is not always achievable owing to the constrained human ability to multitask with optimum efficiency. AI has been implicated in intensive care units over the past many years. In addition to its advantageous applications, this article discusses its disadvantages, prospects, and the changes needed to train future critical care professionals. A comprehensive search of electronic databases was performed using relevant keywords. Data from articles pertinent to the topic was assimilated into this review article.
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Affiliation(s)
- Vinay Suresh
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Kaushal K Singh
- General Medicine, King George's Medical University, Lucknow, IND
| | - Esha Vaish
- Internal Medicine, Mount Sinai Morningside West, New York, USA
| | - Mohan Gurjar
- Critical Care Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, IND
| | | | - Yashita Khulbe
- General Medicine and Surgery, King George's Medical University, Lucknow, IND
| | - Syed Muzaffar
- Critical Care Medicine, King George's Medical University, Lucknow, IND
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19
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Rong Y, Chen Q, Fu Y, Yang X, Al-Hallaq HA, Wu QJ, Yuan L, Xiao Y, Cai B, Latifi K, Benedict SH, Buchsbaum JC, Qi XS. NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions. Int J Radiat Oncol Biol Phys 2024; 119:261-280. [PMID: 37972715 PMCID: PMC11023777 DOI: 10.1016/j.ijrobp.2023.10.033] [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: 02/08/2023] [Revised: 09/16/2023] [Accepted: 10/14/2023] [Indexed: 11/19/2023]
Abstract
Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS. These commercial tools have drawn clinics' attention thanks to their significant benefit in reducing the workload from manual contouring and shortening the duration of treatment planning. However, challenges occur when applying these commercial AI-based segmentation models to diverse clinical scenarios, particularly in uncontrolled environments. Contouring nomenclature and guideline standardization has been the main task undertaken by the NRG Oncology. AI auto-segmentation holds the potential clinical trial participants to reduce interobserver variations, nomenclature non-compliance, and contouring guideline deviations. Meanwhile, trial reviewers could use AI tools to verify contour accuracy and compliance of those submitted datasets. In recognizing the growing clinical utilization and potential of these commercial AI auto-segmentation tools, NRG Oncology has formed a working group to evaluate the clinical utilization and potential of commercial AI auto-segmentation tools. The group will assess in-house and commercially available AI models, evaluation metrics, clinical challenges, and limitations, as well as future developments in addressing these challenges. General recommendations are made in terms of the implementation of these commercial AI models, as well as precautions in recognizing the challenges and limitations.
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Affiliation(s)
- Yi Rong
- Mayo Clinic Arizona, Phoenix, AZ
| | - Quan Chen
- City of Hope Comprehensive Cancer Center Duarte, CA
| | - Yabo Fu
- Memorial Sloan Kettering Cancer Center, Commack, NY
| | | | | | | | - Lulin Yuan
- Virginia Commonwealth University, Richmond, VA
| | - Ying Xiao
- University of Pennsylvania/Abramson Cancer Center, Philadelphia, PA
| | - Bin Cai
- The University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Stanley H Benedict
- University of California Davis Comprehensive Cancer Center, Sacramento, CA
| | | | - X Sharon Qi
- University of California Los Angeles, Los Angeles, CA
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20
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He H, Wang L, Wang X, Zhang M. Artificial intelligence in serum protein electrophoresis: history, state of the art, and perspective. Crit Rev Clin Lab Sci 2024; 61:226-240. [PMID: 37909425 DOI: 10.1080/10408363.2023.2274325] [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: 04/02/2023] [Accepted: 10/19/2023] [Indexed: 11/03/2023]
Abstract
Serum protein electrophoresis (SPEP) is a valuable laboratory test that separates proteins from the blood based on their electrical charge and size. The test can detect and analyze various protein abnormalities, and the interpretation of graphic SPEP features plays a crucial role in the diagnosis and monitoring of conditions, such as myeloma. Furthermore, the advancement of artificial intelligence (AI) technology presents an opportunity to enhance the organization and optimization of analytical procedures by streamlining the process and reducing the potential for human error in SPEP analysis, thereby making the process more efficient and reliable. For instance, AI can assist in the identification of protein peaks, the calculation of their relative proportions, and the detection of abnormalities or inconsistencies. This review explores the characteristics and limitations of AI in SPEP, and the role of standardization in improving its clinical utility. It also offers guidance on the rational ordering and interpreting of SPEP results in conjunction with AI. Such integration can effectively reduce the time and resources required for manual analysis while improving the accuracy and consistency of the results.
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Affiliation(s)
- He He
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Lingfeng Wang
- College of Computer Science, Sichuan University, Chengdu, China
| | - Xia Wang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
| | - Mei Zhang
- Department of Laboratory Medicine, West China Hospital of Sichuan University, Chengdu, China
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21
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Kuo KM, Lin YL, Chang CS, Kuo TJ. An ensemble model for predicting dispositions of emergency department patients. BMC Med Inform Decis Mak 2024; 24:105. [PMID: 38649949 PMCID: PMC11036695 DOI: 10.1186/s12911-024-02503-5] [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: 11/02/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
OBJECTIVE The healthcare challenge driven by an aging population and rising demand is one of the most pressing issues leading to emergency department (ED) overcrowding. An emerging solution lies in machine learning's potential to predict ED dispositions, thus leading to promising substantial benefits. This study's objective is to create a predictive model for ED patient dispositions by employing ensemble learning. It harnesses diverse data types, including structured and unstructured information gathered during ED visits to address the evolving needs of localized healthcare systems. METHODS In this cross-sectional study, 80,073 ED patient records were amassed from a major southern Taiwan hospital in 2018-2019. An ensemble model incorporated structured (demographics, vital signs) and pre-processed unstructured data (chief complaints, preliminary diagnoses) using bag-of-words (BOW) and term frequency-inverse document frequency (TF-IDF). Two random forest base-learners for structured and unstructured data were employed and then complemented by a multi-layer perceptron meta-learner. RESULTS The ensemble model demonstrates strong predictive performance for ED dispositions, achieving an area under the receiver operating characteristic curve of 0.94. The models based on unstructured data encoded with BOW and TF-IDF yield similar performance results. Among the structured features, the top five most crucial factors are age, pulse rate, systolic blood pressure, temperature, and acuity level. In contrast, the top five most important unstructured features are pneumonia, fracture, failure, suspect, and sepsis. CONCLUSIONS Findings indicate that utilizing ensemble learning with a blend of structured and unstructured data proves to be a predictive method for determining ED dispositions.
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Affiliation(s)
- Kuang-Ming Kuo
- Department of Business Management, National United University, No.1, 360301, Lienda, Miaoli, Taiwan
| | - Yih-Lon Lin
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, No. 123, University Road, Section 3, 64002, Douliou, Yunlin, Taiwan
| | - Chao Sheng Chang
- Department of Emergency Medicine, E-Da Hospital, Kaohsiung City, Taiwan.
- Department of Occupational Therapy, I-Shou University, Kaohsiung City, Taiwan.
| | - Tin Ju Kuo
- Department of Computer Science and Information Engineering, National Taitung University, 369, Sec. 2, University Rd, Taitung, Taiwan
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22
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Shannon CP, Lee AH, Tebbutt SJ, Singh A. A Commentary on Multi-omics Data Integration in Systems Vaccinology. J Mol Biol 2024; 436:168522. [PMID: 38458605 DOI: 10.1016/j.jmb.2024.168522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/04/2024] [Accepted: 03/04/2024] [Indexed: 03/10/2024]
Affiliation(s)
| | - Amy Hy Lee
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, Canada
| | - Scott J Tebbutt
- PROOF Centre of Excellence, Vancouver, Canada; Department of Medicine, The University of British Columbia, Vancouver, Canada; Centre for Heart Lung Innovation, Vancouver, Canada
| | - Amrit Singh
- Centre for Heart Lung Innovation, Vancouver, Canada; Department of Anesthesiology, Pharmacology and Therapeutics, The University of British Columbia, Vancouver, Canada.
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23
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Danek BP, Makarious MB, Dadu A, Vitale D, Lee PS, Singleton AB, Nalls MA, Sun J, Faghri F. Federated learning for multi-omics: A performance evaluation in Parkinson's disease. PATTERNS (NEW YORK, N.Y.) 2024; 5:100945. [PMID: 38487808 PMCID: PMC10935499 DOI: 10.1016/j.patter.2024.100945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/29/2024] [Accepted: 02/02/2024] [Indexed: 03/17/2024]
Abstract
While machine learning (ML) research has recently grown more in popularity, its application in the omics domain is constrained by access to sufficiently large, high-quality datasets needed to train ML models. Federated learning (FL) represents an opportunity to enable collaborative curation of such datasets among participating institutions. We compare the simulated performance of several models trained using FL against classically trained ML models on the task of multi-omics Parkinson's disease prediction. We find that FL model performance tracks centrally trained ML models, where the most performant FL model achieves an AUC-PR of 0.876 ± 0.009, 0.014 ± 0.003 less than its centrally trained variation. We also determine that the dispersion of samples within a federation plays a meaningful role in model performance. Our study implements several open-source FL frameworks and aims to highlight some of the challenges and opportunities when applying these collaborative methods in multi-omics studies.
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Affiliation(s)
- Benjamin P. Danek
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
| | - Mary B. Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - Anant Dadu
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
| | - Dan Vitale
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
| | - Paul Suhwan Lee
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew B. Singleton
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mike A. Nalls
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jimeng Sun
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Faraz Faghri
- Center for Alzheimer’s and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
- DataTecnica, Washington, DC 20037, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
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24
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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.
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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
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25
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Eid N. Artificial Intelligence in Pediatric Respiratory Diseases: Current Status and Future Promises. PEDIATRIC ALLERGY, IMMUNOLOGY, AND PULMONOLOGY 2024; 37:1-2. [PMID: 38484266 DOI: 10.1089/ped.2024.0028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Affiliation(s)
- Nemr Eid
- Division of Pulmonology, Allergy & Immunology, University of Louisville, Norton Children's, and University of Louisville School of Medicine, Louisville, Kentucky, USA
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Yang M, Liu S, Hao T, Ma C, Chen H, Li Y, Wu C, Xie J, Qiu H, Li J, Yang Y, Liu C. Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients. Artif Intell Med 2024; 149:102785. [PMID: 38462285 DOI: 10.1016/j.artmed.2024.102785] [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: 09/19/2022] [Revised: 10/05/2023] [Accepted: 01/21/2024] [Indexed: 03/12/2024]
Abstract
Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model. Two external validation populations included 3025 patients from the Philips eICU Research Institute and 2625 patients from Zhongda Hospital Southeast University. A total of 152 intelligently engineered predictors were extracted on an hourly basis. The prediction model referred to as DeepAKI was designed with the basic framework of squeeze-and-excitation networks with dilated causal convolution embedded. The integrated gradients method was utilized to explain the prediction model. When performed on the internal validation set (3175 [15 %] patients from BIDMC) and the two external validation sets, DeepAKI obtained the area under the curve of 0.799 (95 % CI 0.791-0.806), 0.763 (95 % CI 0.755-0.771) and 0.676 (95 % CI 0.668-0.684) for continuousAKI prediction, respectively. For model interpretability, clinically relevant important variables contributing to the model prediction were informed, and individual explanations along the timeline were explored to show how AKI risk arose. The potential threats to generalisability in deep learning-based models when deployed across health systems in real-world settings were analyzed.
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Affiliation(s)
- Meicheng Yang
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Songqiao Liu
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China; Department of Critical Care Medicine, Nanjing Lishui People's Hospital, Zhongda Hospital Lishui Branch, Nanjing, China
| | - Tong Hao
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Caiyun Ma
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Hui Chen
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Yuwen Li
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Changde Wu
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jianfeng Xie
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Haibo Qiu
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jianqing Li
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
| | - Yi Yang
- Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
| | - Chengyu Liu
- The State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing, China.
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27
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Chanci D, Grunwell JR, Rafiei A, Moore R, Bishop NR, Rajapreyar P, Lima LM, Mai M, Kamaleswaran R. Development and Validation of a Model for Endotracheal Intubation and Mechanical Ventilation Prediction in PICU Patients. Pediatr Crit Care Med 2024; 25:212-221. [PMID: 37962125 PMCID: PMC10932861 DOI: 10.1097/pcc.0000000000003410] [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] [Indexed: 11/15/2023]
Abstract
OBJECTIVES To develop and externally validate an intubation prediction model for children admitted to a PICU using objective and routinely available data from the electronic medical records (EMRs). DESIGN Retrospective observational cohort study. SETTING Two PICUs within the same healthcare system: an academic, quaternary care center (36 beds) and a community, tertiary care center (56 beds). PATIENTS Children younger than 18 years old admitted to a PICU between 2010 and 2022. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Clinical data was extracted from the EMR. PICU stays with at least one mechanical ventilation event (≥ 24 hr) occurring within a window of 1-7 days after hospital admission were included in the study. Of 13,208 PICU stays in the derivation PICU cohort, 1,175 (8.90%) had an intubation event. In the validation cohort, there were 1,165 of 17,841 stays (6.53%) with an intubation event. We trained a Categorical Boosting (CatBoost) model using vital signs, laboratory tests, demographic data, medications, organ dysfunction scores, and other patient characteristics to predict the need of intubation and mechanical ventilation using a 24-hour window of data within their hospital stay. We compared the CatBoost model to an extreme gradient boost, random forest, and a logistic regression model. The area under the receiving operating characteristic curve for the derivation cohort and the validation cohort was 0.88 (95% CI, 0.88-0.89) and 0.92 (95% CI, 0.91-0.92), respectively. CONCLUSIONS We developed and externally validated an interpretable machine learning prediction model that improves on conventional clinical criteria to predict the need for intubation in children hospitalized in a PICU using information readily available in the EMR. Implementation of our model may help clinicians optimize the timing of endotracheal intubation and better allocate respiratory and nursing staff to care for mechanically ventilated children.
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Affiliation(s)
- Daniela Chanci
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Jocelyn R Grunwell
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Alireza Rafiei
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Ronald Moore
- Department of Biomedical Informatics, Emory University, Atlanta, GA
| | - Natalie R Bishop
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Prakadeshwari Rajapreyar
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Lisa M Lima
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Mark Mai
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA
- Division of Critical Care Medicine, Children's Healthcare of Atlanta, Atlanta, GA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University, Atlanta, GA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA
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28
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Pruski M. What does it mean for a clinical AI to be just: conflicts between local fairness and being fit-for-purpose? JOURNAL OF MEDICAL ETHICS 2024:jme-2023-109675. [PMID: 38423759 DOI: 10.1136/jme-2023-109675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024]
Abstract
There have been repeated calls to ensure that clinical artificial intelligence (AI) is not discriminatory, that is, it provides its intended benefit to all members of society irrespective of the status of any protected characteristics of individuals in whose healthcare the AI might participate. There have also been repeated calls to ensure that any clinical AI is tailored to the local population in which it is being used to ensure that it is fit-for-purpose. Yet, there might be a clash between these two calls since tailoring an AI to a local population might reduce its effectiveness when the AI is used in the care of individuals who have characteristics which are not represented in the local population. Here, I explore the bioethical concept of local fairness as applied to clinical AI. I first introduce the discussion concerning fairness and inequalities in healthcare and how this problem has continued in attempts to develop AI-enhanced healthcare. I then discuss various technical aspects which might affect the implementation of local fairness. Next, I introduce some rule of law considerations into the discussion to contextualise the issue better by drawing key parallels. I then discuss some potential technical solutions which have been proposed to address the issue of local fairness. Finally, I outline which solutions I consider most likely to contribute to a fit-for-purpose and fair AI.
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Affiliation(s)
- Michal Pruski
- Department of Medical Physics and Clinical Engineering, Cardiff and Vale UHB, Cardiff, UK
- School of Health Sciences, The University of Manchester, Manchester, UK
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29
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Li P, Guo C, Xing Y, Shi Y, Feng L, Zhou F. Core network traffic prediction based on vertical federated learning and split learning. Sci Rep 2024; 14:4663. [PMID: 38409301 PMCID: PMC10897397 DOI: 10.1038/s41598-024-53193-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: 11/02/2023] [Accepted: 01/29/2024] [Indexed: 02/28/2024] Open
Abstract
Wireless traffic prediction is vital for intelligent cellular network operations, such as load-aware resource management and predictive control. Traditional centralized training addresses this but poses issues like excessive data transmission, disregarding delays, and user privacy. Traditional federated learning methods can meet the requirement of jointly training models while protecting the privacy of all parties' data. However, challenges arise when the local data features among participating parties exhibit inconsistency, making the training process difficult to sustain. Our study introduces an innovative framework for wireless traffic prediction based on split learning (SL) and vertical federated learning. Multiple edge clients collaboratively train high-quality prediction models by utilizing diverse traffic data while maintaining the confidentiality of raw data locally. Each participant individually trains dimension-specific prediction models with their respective data, and the outcomes are aggregated through collaboration. A partially global model is formed and shared among clients to address statistical heterogeneity in distributed machine learning. Extensive experiments on real-world datasets demonstrate our method's superiority over current approaches, showcasing its potential for network traffic prediction and accurate forecasting.
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Affiliation(s)
- Pengyu Li
- 6G Research Center, China Telecom Research Institute, Beijing, 102209, China.
| | - Chengwei Guo
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Yanxia Xing
- 6G Research Center, China Telecom Research Institute, Beijing, 102209, China
| | - Yingji Shi
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Lei Feng
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
| | - Fanqin Zhou
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
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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.
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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.
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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.
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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
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32
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Feng X, Shu W, Li M, Li J, Xu J, He M. Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: a cutting edge overview. J Transl Med 2024; 22:131. [PMID: 38310237 PMCID: PMC10837897 DOI: 10.1186/s12967-024-04915-3] [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: 10/31/2023] [Accepted: 01/20/2024] [Indexed: 02/05/2024] Open
Abstract
The capability to gather heterogeneous data, alongside the increasing power of artificial intelligence to examine it, leading a revolution in harnessing multimodal data in the life sciences. However, most approaches are limited to unimodal data, leaving integrated approaches across modalities relatively underdeveloped in computational pathology. Pathogenomics, as an invasive method to integrate advanced molecular diagnostics from genomic data, morphological information from histopathological imaging, and codified clinical data enable the discovery of new multimodal cancer biomarkers to propel the field of precision oncology in the coming decade. In this perspective, we offer our opinions on synthesizing complementary modalities of data with emerging multimodal artificial intelligence methods in pathogenomics. It includes correlation between the pathological and genomic profile of cancer, fusion of histology, and genomics profile of cancer. We also present challenges, opportunities, and avenues for future work.
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Affiliation(s)
- Xiaobing Feng
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Wen Shu
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Mingya Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyu Li
- College of Electrical and Information Engineering, Hunan University, Changsha, China
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Junyao Xu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China
| | - Min He
- College of Electrical and Information Engineering, Hunan University, Changsha, China.
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, Zhejiang, China.
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Soltan AAS, Thakur A, Yang J, Chauhan A, D'Cruz LG, Dickson P, Soltan MA, Thickett DR, Eyre DW, Zhu T, Clifton DA. A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals. Lancet Digit Health 2024; 6:e93-e104. [PMID: 38278619 DOI: 10.1016/s2589-7500(23)00226-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 10/17/2023] [Accepted: 10/30/2023] [Indexed: 01/28/2024]
Abstract
BACKGROUND Multicentre training could reduce biases in medical artificial intelligence (AI); however, ethical, legal, and technical considerations can constrain the ability of hospitals to share data. Federated learning enables institutions to participate in algorithm development while retaining custody of their data but uptake in hospitals has been limited, possibly as deployment requires specialist software and technical expertise at each site. We previously developed an artificial intelligence-driven screening test for COVID-19 in emergency departments, known as CURIAL-Lab, which uses vital signs and blood tests that are routinely available within 1 h of a patient's arrival. Here we aimed to federate our COVID-19 screening test by developing an easy-to-use embedded system-which we introduce as full-stack federated learning-to train and evaluate machine learning models across four UK hospital groups without centralising patient data. METHODS We supplied a Raspberry Pi 4 Model B preloaded with our federated learning software pipeline to four National Health Service (NHS) hospital groups in the UK: Oxford University Hospitals NHS Foundation Trust (OUH; through the locally linked research University, University of Oxford), University Hospitals Birmingham NHS Foundation Trust (UHB), Bedfordshire Hospitals NHS Foundation Trust (BH), and Portsmouth Hospitals University NHS Trust (PUH). OUH, PUH, and UHB participated in federated training, training a deep neural network and logistic regressor over 150 rounds to form and calibrate a global model to predict COVID-19 status, using clinical data from patients admitted before the pandemic (COVID-19-negative) and testing positive for COVID-19 during the first wave of the pandemic. We conducted a federated evaluation of the global model for admissions during the second wave of the pandemic at OUH, PUH, and externally at BH. For OUH and PUH, we additionally performed local fine-tuning of the global model using the sites' individual training data, forming a site-tuned model, and evaluated the resultant model for admissions during the second wave of the pandemic. This study included data collected between Dec 1, 2018, and March 1, 2021; the exact date ranges used varied by site. The primary outcome was overall model performance, measured as the area under the receiver operating characteristic curve (AUROC). Removable micro secure digital (microSD) storage was destroyed on study completion. FINDINGS Clinical data from 130 941 patients (1772 COVID-19-positive), routinely collected across three hospital groups (OUH, PUH, and UHB), were included in federated training. The evaluation step included data from 32 986 patients (3549 COVID-19-positive) attending OUH, PUH, or BH during the second wave of the pandemic. Federated training of a global deep neural network classifier improved upon performance of models trained locally in terms of AUROC by a mean of 27·6% (SD 2·2): AUROC increased from 0·574 (95% CI 0·560-0·589) at OUH and 0·622 (0·608-0·637) at PUH using the locally trained models to 0·872 (0·862-0·882) at OUH and 0·876 (0·865-0·886) at PUH using the federated global model. Performance improvement was smaller for a logistic regression model, with a mean increase in AUROC of 13·9% (0·5%). During federated external evaluation at BH, AUROC for the global deep neural network model was 0·917 (0·893-0·942), with 89·7% sensitivity (83·6-93·6) and 76·6% specificity (73·9-79·1). Site-specific tuning of the global model did not significantly improve performance (change in AUROC <0·01). INTERPRETATION We developed an embedded system for federated learning, using microcomputing to optimise for ease of deployment. We deployed full-stack federated learning across four UK hospital groups to develop a COVID-19 screening test without centralising patient data. Federation improved model performance, and the resultant global models were generalisable. Full-stack federated learning could enable hospitals to contribute to AI development at low cost and without specialist technical expertise at each site. FUNDING The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund.
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Affiliation(s)
- Andrew A S Soltan
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Department of Oncology, University of Oxford, Oxford, UK; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - Anshul Thakur
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Jenny Yang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Anoop Chauhan
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | - Leon G D'Cruz
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK
| | | | - Marina A Soltan
- The Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - David R Thickett
- The Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - David W Eyre
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK; NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford and Public Health England, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Tingting Zhu
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford, UK; Oxford-Suzhou Centre for Advanced Research, Suzhou, China
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Kaggie JD. Addressing machine learning challenges with microcomputing and federated learning. Lancet Digit Health 2024; 6:e88-e89. [PMID: 38278617 DOI: 10.1016/s2589-7500(23)00266-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/28/2024]
Affiliation(s)
- Joshua D Kaggie
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK.
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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.
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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.
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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.
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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
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Parida A, Anwar SM, Patel MP, Blom M, Einat TT, Tonetti A, Baror Y, Dayan I, Linguraru MG. CAFES: Chest X-ray Analysis using Federated Self-supervised Learning for Pediatric COVID-19 Detection. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12927:129271I. [PMID: 38873338 PMCID: PMC11167651 DOI: 10.1117/12.3008757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.
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Affiliation(s)
- Abhijeet Parida
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, 111 Michigan Ave, Washington, DC 20010, USA
| | - Syed Muhammad Anwar
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, 111 Michigan Ave, Washington, DC 20010, USA
- School of Medicine and Health Sciences, George Washington University, 2121 I St NW, Washington, DC 20052, USA
| | | | | | | | | | - Yuval Baror
- Rhino Health, 22 Boston Wharf Rd, MA 02210, USA
| | - Ittai Dayan
- Rhino Health, 22 Boston Wharf Rd, MA 02210, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, 111 Michigan Ave, Washington, DC 20010, USA
- School of Medicine and Health Sciences, George Washington University, 2121 I St NW, Washington, DC 20052, USA
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Wang G. Making "CASES" for AI in Medicine. BME FRONTIERS 2024; 5:0036. [PMID: 38288398 PMCID: PMC10823727 DOI: 10.34133/bmef.0036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 01/01/2024] [Indexed: 01/31/2024] Open
Abstract
In this perspective, "CASES" are made for AI in medicine. The CASES mean Confidence, Adaptability, Stability, Explainability, and Security of AI systems. We underline that these CASES can be addressed not only individually but also synergistically on the large model platform and using cutting-edge diffusion-type models.
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Affiliation(s)
- Ge Wang
- Biomedical Imaging Center, Center for Computational Innovations, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, School of Engineering,
Rensselaer Polytechnic Institute, Troy, NY, USA
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Pan W, Xu Z, Rajendran S, Wang F. An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals. PATTERNS (NEW YORK, N.Y.) 2024; 5:100898. [PMID: 38264713 PMCID: PMC10801228 DOI: 10.1016/j.patter.2023.100898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/06/2023] [Accepted: 11/21/2023] [Indexed: 01/25/2024]
Abstract
Clinical risk prediction with electronic health records (EHR) using machine learning has attracted lots of attentions in recent years, where one of the key challenges is how to protect data privacy. Federated learning (FL) provides a promising framework for building predictive models by leveraging the data from multiple institutions without sharing them. However, data distribution drift across different institutions greatly impacts the performance of FL. In this paper, an adaptive FL framework was proposed to address this challenge. Our framework separated the input features into stable, domain-specific, and conditional-irrelevant parts according to their relationships to clinical outcomes. We evaluate this framework on the tasks of predicting the onset risk of sepsis and acute kidney injury (AKI) for patients in the intensive care unit (ICU) from multiple clinical institutions. The results showed that our framework can achieve better prediction performance compared with existing FL baselines and provide reasonable feature interpretations.
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Affiliation(s)
- Weishen Pan
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Zhenxing Xu
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Suraj Rajendran
- Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
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Tong L, Shi W, Isgut M, Zhong Y, Lais P, Gloster L, Sun J, Swain A, Giuste F, Wang MD. Integrating Multi-Omics Data With EHR for Precision Medicine Using Advanced Artificial Intelligence. IEEE Rev Biomed Eng 2024; 17:80-97. [PMID: 37824325 DOI: 10.1109/rbme.2023.3324264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
With the recent advancement of novel biomedical technologies such as high-throughput sequencing and wearable devices, multi-modal biomedical data ranging from multi-omics molecular data to real-time continuous bio-signals are generated at an unprecedented speed and scale every day. For the first time, these multi-modal biomedical data are able to make precision medicine close to a reality. However, due to data volume and the complexity, making good use of these multi-modal biomedical data requires major effort. Researchers and clinicians are actively developing artificial intelligence (AI) approaches for data-driven knowledge discovery and causal inference using a variety of biomedical data modalities. These AI-based approaches have demonstrated promising results in various biomedical and healthcare applications. In this review paper, we summarize the state-of-the-art AI models for integrating multi-omics data and electronic health records (EHRs) for precision medicine. We discuss the challenges and opportunities in integrating multi-omics data with EHRs and future directions. We hope this review can inspire future research and developing in integrating multi-omics data with EHRs for precision medicine.
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Li Z, Yan C, Zhang X, Gharibi G, Yin Z, Jiang X, Malin BA. Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2024; 2023:1047-1056. [PMID: 38222326 PMCID: PMC10785879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.
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Affiliation(s)
| | - Chao Yan
- Vanderbilt University Medical Center, Nashville, TN
| | | | | | - Zhijun Yin
- Vanderbilt University, Nashville, TN
- Vanderbilt University Medical Center, Nashville, TN
| | | | - Bradley A Malin
- Vanderbilt University, Nashville, TN
- Vanderbilt University Medical Center, Nashville, TN
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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.
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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
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Dellacasa C, Ortali M, Rossi E, Abu Attieh H, Osmo T, Puskaric M, Rinaldi E, Prasser F, Stellmach C, Cataudella S, Agarwal B, Mata Naranjo J, Scipione G. An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation. Digit Health 2024; 10:20552076241248922. [PMID: 38766364 PMCID: PMC11100396 DOI: 10.1177/20552076241248922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 04/04/2024] [Indexed: 05/22/2024] Open
Abstract
Background The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR). Methods The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation. Results The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2. Conclusions Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.
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Affiliation(s)
- Chiara Dellacasa
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Maurizio Ortali
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Elisa Rossi
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
| | - Hammam Abu Attieh
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Osmo
- Département Archivage et Services aux Données (DASD), Centre Informatique National de l'Enseignement Supérieur (CINES), Montpellier, France
| | - Miroslav Puskaric
- High Performance Computing Center Stuttgart (HLRS), University of Stuttgart, Stuttgart, Germany
| | - Eugenia Rinaldi
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Fabian Prasser
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Caroline Stellmach
- Berlin Institute of Health (BIH), Charité – Universitätsmedizin Berlin, Berlin, Germany
| | | | - Bhaskar Agarwal
- HPC Department, CINECA Consorzio Interuniversitario,
Bologna, Italy
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Bednorz A, Mak JKL, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging 2023; 18:2171-2183. [PMID: 38152074 PMCID: PMC10752027 DOI: 10.2147/cia.s400887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/05/2023] [Indexed: 12/29/2023] Open
Abstract
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.
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Affiliation(s)
- Adam Bednorz
- John Paul II Geriatric Hospital, Katowice, Poland
- Institute of Psychology, Humanitas Academy, Sosnowiec, Poland
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
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Huang B, Hu S, Liu Z, Lin CL, Su J, Zhao C, Wang L, Wang W. Challenges and prospects of visual contactless physiological monitoring in clinical study. NPJ Digit Med 2023; 6:231. [PMID: 38097771 PMCID: PMC10721846 DOI: 10.1038/s41746-023-00973-x] [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: 07/02/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023] Open
Abstract
The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.
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Affiliation(s)
- Bin Huang
- AI Research Center, Hangzhou Innovation Institute, Beihang University, 99 Juhang Rd., Binjiang Dist., Hangzhou, Zhejiang, China.
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
| | - Shen Hu
- Department of Obstetrics, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Epidemiology, The Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zimeng Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Chun-Liang Lin
- College of Electrical Engineering and Computer Science, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung, Taiwan.
| | - Junfeng Su
- Department of General Intensive Care Unit, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Key Laboratory of Early Warning and Intervention of Multiple Organ Failure, China National Ministry of Education, Hangzhou, Zhejiang, China
| | - Changchen Zhao
- AI Research Center, Hangzhou Innovation Institute, Beihang University, 99 Juhang Rd., Binjiang Dist., Hangzhou, Zhejiang, China
| | - Li Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenjin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, 1088 Xueyuan Ave, Nanshan Dist., Shenzhen, Guangdong, China.
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Fauveau V, Sun S, Liu Z, Mei X, Grant J, Sullivan M, Greenspan H, Feng L, Fayad ZA. Discovery Viewer (DV): Web-Based Medical AI Model Development Platform and Deployment Hub. Bioengineering (Basel) 2023; 10:1396. [PMID: 38135987 PMCID: PMC10741011 DOI: 10.3390/bioengineering10121396] [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: 11/07/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
The rapid rise of artificial intelligence (AI) in medicine in the last few years highlights the importance of developing bigger and better systems for data and model sharing. However, the presence of Protected Health Information (PHI) in medical data poses a challenge when it comes to sharing. One potential solution to mitigate the risk of PHI breaches is to exclusively share pre-trained models developed using private datasets. Despite the availability of these pre-trained networks, there remains a need for an adaptable environment to test and fine-tune specific models tailored for clinical tasks. This environment should be open for peer testing, feedback, and continuous model refinement, allowing dynamic model updates that are especially important in the medical field, where diseases and scanning techniques evolve rapidly. In this context, the Discovery Viewer (DV) platform was developed in-house at the Biomedical Engineering and Imaging Institute at Mount Sinai (BMEII) to facilitate the creation and distribution of cutting-edge medical AI models that remain accessible after their development. The all-in-one platform offers a unique environment for non-AI experts to learn, develop, and share their own deep learning (DL) concepts. This paper presents various use cases of the platform, with its primary goal being to demonstrate how DV holds the potential to empower individuals without expertise in AI to create high-performing DL models. We tasked three non-AI experts to develop different musculoskeletal AI projects that encompassed segmentation, regression, and classification tasks. In each project, 80% of the samples were provided with a subset of these samples annotated to aid the volunteers in understanding the expected annotation task. Subsequently, they were responsible for annotating the remaining samples and training their models through the platform's "Training Module". The resulting models were then tested on the separate 20% hold-off dataset to assess their performance. The classification model achieved an accuracy of 0.94, a sensitivity of 0.92, and a specificity of 1. The regression model yielded a mean absolute error of 14.27 pixels. And the segmentation model attained a Dice Score of 0.93, with a sensitivity of 0.9 and a specificity of 0.99. This initiative seeks to broaden the community of medical AI model developers and democratize the access of this technology to all stakeholders. The ultimate goal is to facilitate the transition of medical AI models from research to clinical settings.
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Affiliation(s)
- Valentin Fauveau
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
| | - Sean Sun
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Zelong Liu
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
| | - Xueyan Mei
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
| | - James Grant
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
| | - Mikey Sullivan
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
| | - Hayit Greenspan
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
| | - Li Feng
- Center for Advanced Imaging Innovation and Research (CAIR), NYU Grossman School of Medicine, New York, NY 10016, USA;
| | - Zahi A. Fayad
- BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; (Z.L.); (X.M.); (J.G.); (M.S.); (H.G.); (Z.A.F.)
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Tao S, Liu H, Sun C, Ji H, Ji G, Han Z, Gao R, Ma J, Ma R, Chen Y, Fu S, Wang Y, Sun Y, Rong Y, Zhang X, Zhou G, Sun H. Collaborative and privacy-preserving retired battery sorting for profitable direct recycling via federated machine learning. Nat Commun 2023; 14:8032. [PMID: 38052823 DOI: 10.1038/s41467-023-43883-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
Unsorted retired batteries with varied cathode materials hinder the adoption of direct recycling due to their cathode-specific nature. The surge in retired batteries necessitates precise sorting for effective direct recycling, but challenges arise from varying operational histories, diverse manufacturers, and data privacy concerns of recycling collaborators (data owners). Here we show, from a unique dataset of 130 lithium-ion batteries spanning 5 cathode materials and 7 manufacturers, a federated machine learning approach can classify these retired batteries without relying on past operational data, safeguarding the data privacy of recycling collaborators. By utilizing the features extracted from the end-of-life charge-discharge cycle, our model exhibits 1% and 3% cathode sorting errors under homogeneous and heterogeneous battery recycling settings respectively, attributed to our innovative Wasserstein-distance voting strategy. Economically, the proposed method underscores the value of precise battery sorting for a prosperous and sustainable recycling industry. This study heralds a new paradigm of using privacy-sensitive data from diverse sources, facilitating collaborative and privacy-respecting decision-making for distributed systems.
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Affiliation(s)
- Shengyu Tao
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Haizhou Liu
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Chongbo Sun
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Haocheng Ji
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Guanjun Ji
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Zhiyuan Han
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Runhua Gao
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Jun Ma
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Ruifei Ma
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Yuou Chen
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Shiyi Fu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yu Wang
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yaojie Sun
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Yu Rong
- Tencent AI Lab, Tencent, Shenzhen, China
| | - Xuan Zhang
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
| | - Guangmin Zhou
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
| | - Hongbin Sun
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
- Department of Electrical Engineering, Tsinghua University, Beijing, China.
- College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China.
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48
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Huang CT, Wang TJ, Kuo LK, Tsai MJ, Cia CT, Chiang DH, Chang PJ, Chong IW, Tsai YS, Chu YC, Liu CJ, Chen CH, Pai KC, Wu CL. Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan. Health Inf Sci Syst 2023; 11:48. [PMID: 37822805 PMCID: PMC10562351 DOI: 10.1007/s13755-023-00248-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
Purpose To address the contentious data sharing across hospitals, this study adopted a novel approach, federated learning (FL), to establish an aggregate model for acute kidney injury (AKI) prediction in critically ill patients in Taiwan. Methods This study used data from the Critical Care Database of Taichung Veterans General Hospital (TCVGH) from 2015 to 2020 and electrical medical records of the intensive care units (ICUs) between 2018 and 2020 of four referral centers in different areas across Taiwan. AKI prediction models were trained and validated thereupon. An FL-based prediction model across hospitals was then established. Results The study included 16,732 ICU admissions from the TCVGH and 38,424 ICU admissions from the other four hospitals. The complete model with 60 features and the parsimonious model with 21 features demonstrated comparable accuracies using extreme gradient boosting, neural network (NN), and random forest, with an area under the receiver-operating characteristic (AUROC) curve of approximately 0.90. The Shapley Additive Explanations plot demonstrated that the selected features were the key clinical components of AKI for critically ill patients. The AUROC curve of the established parsimonious model for external validation at the four hospitals ranged from 0.760 to 0.865. NN-based FL slightly improved the model performance at the four centers. Conclusion A reliable prediction model for AKI in ICU patients was developed with a lead time of 24 h, and it performed better when the novel FL platform across hospitals was implemented. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-023-00248-5.
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Affiliation(s)
- Chun-Te Huang
- Institute of Emergency and Critical Care Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
- Nephrology and Critical Care Medicine, Department of Internal Medicine and Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Tsai-Jung Wang
- Nephrology and Critical Care Medicine, Department of Internal Medicine and Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Li-Kuo Kuo
- Department of Critical Care Medicine, MacKay Memorial Hospital, Taipei, Taiwan
| | - Ming-Ju Tsai
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Cong-Tat Cia
- Division of Critical Care Medicine, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Dung-Hung Chiang
- Department of Critical Care Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Po-Jen Chang
- Department of Information Technology, MacKay Memorial Hospital, Taipei, Taiwan
| | - Inn-Wen Chong
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Yi-Shan Tsai
- Department of Diagnostic Radiology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Yuan-Chia Chu
- Department of Information Technology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chia-Jen Liu
- Institute of Emergency and Critical Care Medicine, National Yang-Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Hsu Chen
- Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Kai-Chih Pai
- College of Engineering, Tunghai University, Taichung, Taiwan
| | - Chieh-Liang Wu
- College of Medicine, National Chung Hshin University, Taichung, Taiwan
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49
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Kang Z, Margolis DJ, Wang S, Li Q, Song J, Wang L. Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions. Curr Urol Rep 2023; 24:561-570. [PMID: 37936016 DOI: 10.1007/s11934-023-01187-0] [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] [Accepted: 10/21/2023] [Indexed: 11/09/2023]
Abstract
PURPOSE OF REVIEW Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions present a clinical dilemma due to their uncertain nature, which complicates the development of a definitive management strategy. These lesions have an incidence rate of approximately 22-32%, with clinically significant prostate cancer (csPCa) accounting for about 10-30%. Therefore, a thorough evaluation is warranted. RECENT FINDINGS This review highlights the need for radiology peer review, including the confirmation of dynamic contrast-enhanced (DCE) compliance, as the initial step. Additional MRI models such as VERDICT or Tofts need to be verified. Current evidence shows that imaging and clinical indicators can be used for risk stratification of PI-RADS 3 lesions. For low-risk lesions, a safety net monitoring approach involving annual repeat MRI can be employed. In contrast, lesions deemed potentially risky based on prostate-specific antigen density (PSAD), 68 Ga-PSMA PET/CT, MPS, Proclarix, or AI/machine learning models should undergo biopsy. It is recommended to establish a multidisciplinary team that takes into account factors such as age, PSAD, prostate, and lesion size, as well as previous biopsy pathological findings. Combining expert opinions, clinical-imaging indicators, and emerging methods will contribute to the development of management strategies for PI-RADS 3 lesions.
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Affiliation(s)
- Zhen Kang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 36 Yong'an Rd, Xicheng District, Beijing, 100016, China
| | - Daniel J Margolis
- Department of Radiology, Weill Cornell Medicine/New York Presbyterian, New York, NY, USA
| | - Shaogang Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiubai Li
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jian Song
- Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 36 Yong'an Rd, Xicheng District, Beijing, 100016, China.
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50
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Tan AZ, Yu H, Cui L, Yang Q. Towards Personalized Federated Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:9587-9603. [PMID: 35344498 DOI: 10.1109/tnnls.2022.3160699] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI research, there has been growing awareness and concerns of data privacy. Recent significant developments in the data regulation landscape have prompted a seismic shift in interest toward privacy-preserving AI. This has contributed to the popularity of Federated Learning (FL), the leading paradigm for the training of machine learning models on data silos in a privacy-preserving manner. In this survey, we explore the domain of personalized FL (PFL) to address the fundamental challenges of FL on heterogeneous data, a universal characteristic inherent in all real-world datasets. We analyze the key motivations for PFL and present a unique taxonomy of PFL techniques categorized according to the key challenges and personalization strategies in PFL. We highlight their key ideas, challenges, opportunities, and envision promising future trajectories of research toward a new PFL architectural design, realistic PFL benchmarking, and trustworthy PFL approaches.
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