Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence
Authors
Bai, Xiang
Wang, Hanchen
Ma, Liya
Xu, Yongchao
Fan, Ziwei
Yang, Fan
Ma, Ke
Yang, Jiehua
Bai, Song
Shu, Chang
Zou, Xinyu
Huang, Renhao
Zhang, Changzheng
Liu, Xiaowu
Tu, Dandan
Xu, Chuou
Zhang, Wenqing
Wang, Xi
Chen, Anguo
Zeng, Yu
Holalkere, Nagaraj
Halin, Neil J.
Kamel, Ihab R.
Peng, Xuehua
Wang, Xiang
Shao, Jianbo
Liu, Weiyang
Teng, Zhongzhao
Beer, Lucian
Sala, Evis
Weller, Adrian
Lasenby, Joan
Zheng, Chuansheng
Wang, Jianming
Schönlieb, Carola
Xia, Tian
Publication Date
2021-12-15Journal Title
Nature Machine Intelligence
Publisher
Nature Publishing Group UK
Volume
3
Issue
12
Pages
1081-1089
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., et al. (2021). Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. Nature Machine Intelligence, 3 (12), 1081-1089. https://doi.org/10.1038/s42256-021-00421-z
Abstract
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.
Keywords
Article, /4000/4008, /692/700, /639/705/1042, /631/326/596/4130, article
Identifiers
s42256-021-00421-z, 421
External DOI: https://doi.org/10.1038/s42256-021-00421-z
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335917
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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