Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

Abstract Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interp...

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Autores principales:	Qi Dou, Tiffany Y. So, Meirui Jiang, Quande Liu, Varut Vardhanabhuti, Georgios Kaissis, Zeju Li, Weixin Si, Heather H. C. Lee, Kevin Yu, Zuxin Feng, Li Dong, Egon Burian, Friederike Jungmann, Rickmer Braren, Marcus Makowski, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Simon C. H. Yu, Pheng Ann Heng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/340f463aed8748e1b87b2603e67e7d80
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spelling	oai:doaj.org-article:340f463aed8748e1b87b2603e67e7d802021-12-02T14:23:33ZFederated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study10.1038/s41746-021-00431-62398-6352https://doaj.org/article/340f463aed8748e1b87b2603e67e7d802021-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00431-6https://doaj.org/toc/2398-6352Abstract Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.Qi DouTiffany Y. SoMeirui JiangQuande LiuVarut VardhanabhutiGeorgios KaissisZeju LiWeixin SiHeather H. C. LeeKevin YuZuxin FengLi DongEgon BurianFriederike JungmannRickmer BrarenMarcus MakowskiBernhard KainzDaniel RueckertBen GlockerSimon C. H. YuPheng Ann HengNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Qi Dou Tiffany Y. So Meirui Jiang Quande Liu Varut Vardhanabhuti Georgios Kaissis Zeju Li Weixin Si Heather H. C. Lee Kevin Yu Zuxin Feng Li Dong Egon Burian Friederike Jungmann Rickmer Braren Marcus Makowski Bernhard Kainz Daniel Rueckert Ben Glocker Simon C. H. Yu Pheng Ann Heng Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
description	Abstract Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.
format	article
author	Qi Dou Tiffany Y. So Meirui Jiang Quande Liu Varut Vardhanabhuti Georgios Kaissis Zeju Li Weixin Si Heather H. C. Lee Kevin Yu Zuxin Feng Li Dong Egon Burian Friederike Jungmann Rickmer Braren Marcus Makowski Bernhard Kainz Daniel Rueckert Ben Glocker Simon C. H. Yu Pheng Ann Heng
author_facet	Qi Dou Tiffany Y. So Meirui Jiang Quande Liu Varut Vardhanabhuti Georgios Kaissis Zeju Li Weixin Si Heather H. C. Lee Kevin Yu Zuxin Feng Li Dong Egon Burian Friederike Jungmann Rickmer Braren Marcus Makowski Bernhard Kainz Daniel Rueckert Ben Glocker Simon C. H. Yu Pheng Ann Heng
author_sort	Qi Dou
title	Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
title_short	Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
title_full	Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
title_fullStr	Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
title_full_unstemmed	Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study
title_sort	federated deep learning for detecting covid-19 lung abnormalities in ct: a privacy-preserving multinational validation study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/340f463aed8748e1b87b2603e67e7d80
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Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study

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