Publication: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
2
Issued Date
2021-12-01
Resource Type
ISSN
25225839
Other identifier(s)
2-s2.0-85121384145
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Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Nature Machine Intelligence. Vol.3, No.12 (2021), 1081-1089
Suggested Citation
Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena E. Sanchez, Evis Sala, Daniel L. Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola Schönlieb, Tian Xia Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nature Machine Intelligence. Vol.3, No.12 (2021), 1081-1089. doi:10.1038/s42256-021-00421-z Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/76625
Research Projects
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Title
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Author(s)
Xiang Bai
Hanchen Wang
Liya Ma
Yongchao Xu
Jiefeng Gan
Ziwei Fan
Fan Yang
Ke Ma
Jiehua Yang
Song Bai
Chang Shu
Xinyu Zou
Renhao Huang
Changzheng Zhang
Xiaowu Liu
Dandan Tu
Chuou Xu
Wenqing Zhang
Xi Wang
Anguo Chen
Yu Zeng
Dehua Yang
Ming Wei Wang
Nagaraj Holalkere
Neil J. Halin
Ihab R. Kamel
Jia Wu
Xuehua Peng
Xiang Wang
Jianbo Shao
Pattanasak Mongkolwat
Jianjun Zhang
Weiyang Liu
Michael Roberts
Zhongzhao Teng
Lucian Beer
Lorena E. Sanchez
Evis Sala
Daniel L. Rubin
Adrian Weller
Joan Lasenby
Chuangsheng Zheng
Jianming Wang
Zhen Li
Carola Schönlieb
Tian Xia
Hanchen Wang
Liya Ma
Yongchao Xu
Jiefeng Gan
Ziwei Fan
Fan Yang
Ke Ma
Jiehua Yang
Song Bai
Chang Shu
Xinyu Zou
Renhao Huang
Changzheng Zhang
Xiaowu Liu
Dandan Tu
Chuou Xu
Wenqing Zhang
Xi Wang
Anguo Chen
Yu Zeng
Dehua Yang
Ming Wei Wang
Nagaraj Holalkere
Neil J. Halin
Ihab R. Kamel
Jia Wu
Xuehua Peng
Xiang Wang
Jianbo Shao
Pattanasak Mongkolwat
Jianjun Zhang
Weiyang Liu
Michael Roberts
Zhongzhao Teng
Lucian Beer
Lorena E. Sanchez
Evis Sala
Daniel L. Rubin
Adrian Weller
Joan Lasenby
Chuangsheng Zheng
Jianming Wang
Zhen Li
Carola Schönlieb
Tian Xia
Other Contributor(s)
Department of Radiology
Department of Engineering
Faculty of Mathematics
Alan Turing Institute
Stanford University School of Medicine
Shanghai Institute of Materia Medica, Chinese Academy of Sciences
Huazhong University of Science and Technology
Tufts University
University of Texas MD Anderson Cancer Center
Wuhan University of Science and Technology
Mahidol University
Stanford University
AstraZeneca
The Central Hospital of Wuhan
The Johns Hopkins Hospital
Tongji Medical College
MSA Capital
CalmCar Inc
Wuhan Children's Hospital
Wuhan Blood Centre
Department of Engineering
Faculty of Mathematics
Alan Turing Institute
Stanford University School of Medicine
Shanghai Institute of Materia Medica, Chinese Academy of Sciences
Huazhong University of Science and Technology
Tufts University
University of Texas MD Anderson Cancer Center
Wuhan University of Science and Technology
Mahidol University
Stanford University
AstraZeneca
The Central Hospital of Wuhan
The Johns Hopkins Hospital
Tongji Medical College
MSA Capital
CalmCar Inc
Wuhan Children's Hospital
Wuhan Blood Centre
Abstract
Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.