{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:28:01Z","timestamp":1778257681668,"version":"3.51.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T00:00:00Z","timestamp":1616976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1038\/s41746-021-00431-6","type":"journal-article","created":{"date-parts":[[2021,3,29]],"date-time":"2021-03-29T10:03:03Z","timestamp":1617012183000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":229,"title":["Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3416-9950","authenticated-orcid":false,"given":"Qi","family":"Dou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8268-0721","authenticated-orcid":false,"given":"Tiffany Y.","family":"So","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4228-8420","authenticated-orcid":false,"given":"Meirui","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Quande","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6677-3194","authenticated-orcid":false,"given":"Varut","family":"Vardhanabhuti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8382-8062","authenticated-orcid":false,"given":"Georgios","family":"Kaissis","sequence":"additional","affiliation":[]},{"given":"Zeju","family":"Li","sequence":"additional","affiliation":[]},{"given":"Weixin","family":"Si","sequence":"additional","affiliation":[]},{"given":"Heather H. C.","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zuxin","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Li","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Egon","family":"Burian","sequence":"additional","affiliation":[]},{"given":"Friederike","family":"Jungmann","sequence":"additional","affiliation":[]},{"given":"Rickmer","family":"Braren","sequence":"additional","affiliation":[]},{"given":"Marcus","family":"Makowski","sequence":"additional","affiliation":[]},{"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5683-5889","authenticated-orcid":false,"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4897-9356","authenticated-orcid":false,"given":"Ben","family":"Glocker","sequence":"additional","affiliation":[]},{"given":"Simon C. H.","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Pheng Ann","family":"Heng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,29]]},"reference":[{"key":"431_CR1","unstructured":"COVID C. Global cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). ArcGIS. Johns Hopkins CSSE (2020)."},{"key":"431_CR2","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1038\/s41591-020-0824-5","volume":"26","author":"DSW Ting","year":"2020","unstructured":"Ting, D. S. W., Carin, L., Dzau, V. & Wong, T. Y. Digital technology and COVID-19. Nat. Med. 26, 459\u2013461 (2020).","journal-title":"Nat. Med."},{"key":"431_CR3","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1038\/s42256-020-0181-6","volume":"2","author":"N Peiffer-Smadja","year":"2020","unstructured":"Peiffer-Smadja, N. et al. Machine learning for COVID-19 needs global collaboration and data-sharing. Nat. Mach. Intell. 2, 293\u2013294 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"431_CR4","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1038\/s42256-020-0184-3","volume":"2","author":"M Luengo-Oroz","year":"2020","unstructured":"Luengo-Oroz, M. et al. Artificial intelligence cooperation to support the global response to COVID-19. Nat. Mach. Intell. 2, 295\u2013297 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"431_CR5","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342\u20131350 (2018).","journal-title":"Nat. Med."},{"key":"431_CR6","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1038\/s42256-020-0186-1","volume":"2","author":"GA Kaissis","year":"2020","unstructured":"Kaissis, G. A., Makowski, M. R., R\u00fcckert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305\u2013311 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"431_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-020-00323-1","volume":"3","author":"N Rieke","year":"2020","unstructured":"Rieke, N. et al. The future of digital health with federated learning. npj Digital Med. 3, 1\u20137 (2020).","journal-title":"npj Digital Med."},{"key":"431_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-019-0148-3","volume":"2","author":"P Shah","year":"2019","unstructured":"Shah, P. et al. Artificial intelligence and machine learning in clinical development: a translational perspective. npj Digital Med. 2, 1\u20135 (2019).","journal-title":"npj Digital Med."},{"key":"431_CR9","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24\u201329 (2019).","journal-title":"Nat. Med."},{"key":"431_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-69250-1","volume":"10","author":"MJ Sheller","year":"2020","unstructured":"Sheller, M. J. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 1\u20132 (2020).","journal-title":"Sci. Rep."},{"key":"431_CR11","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1038\/s42256-020-0180-7","volume":"2","author":"L Yan","year":"2020","unstructured":"Yan, L. et al. An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell. 2, 283\u2013288 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"431_CR12","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1038\/s42256-020-0185-2","volume":"2","author":"Y Hu","year":"2020","unstructured":"Hu, Y. et al. The challenges of deploying artificial intelligence models in a rapidly evolving pandemic. Nat. Mach. Intell. 2, 298\u2013300 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"431_CR13","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.3390\/jcm9051514","volume":"9","author":"E Burian","year":"2020","unstructured":"Burian, E. et al. Intensive care risk estimation in COVID-19 pneumonia based on clinical and imaging parameters: experiences from the munich cohort. J. Clin. Med. 9, 1514 (2020).","journal-title":"J. Clin. Med."},{"key":"431_CR14","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1148\/radiol.2020200463","volume":"295","author":"A Bernheim","year":"2020","unstructured":"Bernheim, A. et al. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology 295, 685\u2013691 (2020).","journal-title":"Radiology"},{"key":"431_CR15","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.cell.2020.04.045","volume":"181","author":"K Zhang","year":"2020","unstructured":"Zhang, K. et al. Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181, 1423\u20131433 (2020).","journal-title":"Cell"},{"key":"431_CR16","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi, F. et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 14, 4\u201315 (2020).","journal-title":"IEEE Rev. Biomed. Eng"},{"key":"431_CR17","first-page":"1197","volume":"18","author":"J Ma","year":"2020","unstructured":"Ma, J. et al. Towards data-efficient learning: a benchmark for COVID-19 CT lung and infection segmentation. Med. Phys. 18, 1197\u20131210 (2020).","journal-title":"Med. Phys"},{"key":"431_CR18","doi-asserted-by":"crossref","unstructured":"Zlocha, M., Dou, Q. & Glocker, B. Improving retinanet for CT lesion detection with dense masks from weak RECIST labels. International Conference on Medical Image Computing and Computer-Assisted Intervention 402\u2013410 (2019).","DOI":"10.1007\/978-3-030-32226-7_45"},{"key":"431_CR19","doi-asserted-by":"publisher","first-page":"036501","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L. & Summers, R. M. DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5, 036501 (2018).","journal-title":"J. Med. Imaging"},{"key":"431_CR20","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley, J. A. & McNeil, B. J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29\u201336 (1982).","journal-title":"Radiology"},{"key":"431_CR21","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing areas under two or more correlated receiver operating characteristics curves: a nonparametric approach. Biometrics 44, 837\u2013845 (1988).","journal-title":"Biometrics"},{"key":"431_CR22","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process Syst. 28, 91\u201399 (2015).","journal-title":"Adv. Neural Inf. Process Syst"},{"key":"431_CR23","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1093\/biomet\/26.4.404","volume":"26","author":"CJ Clopper","year":"1934","unstructured":"Clopper, C. J. & Pearson, E. S. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika 26, 404\u2013413 (1934).","journal-title":"Biometrika"},{"key":"431_CR24","first-page":"494","volume":"1","author":"B Ci","year":"1987","unstructured":"Ci, B. & Rule, R. O. Confidence intervals. Lancet 1, 494\u2013497 (1987).","journal-title":"Lancet"},{"key":"431_CR25","doi-asserted-by":"publisher","first-page":"2713","DOI":"10.1109\/TMI.2020.2974574","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Dou, Q., Yu, L. & Heng, P. A. MS-Net: multi-site network for improving prostate segmentation with heterogeneous MRI data. IEEE Trans. Med. Imaging 39, 2713\u20132724 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"431_CR26","doi-asserted-by":"publisher","first-page":"e1002683","DOI":"10.1371\/journal.pmed.1002683","volume":"15","author":"JR Zech","year":"2018","unstructured":"Zech, J. R. et al. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study. PLoS Med. 15, e1002683 (2018).","journal-title":"PLoS Med."},{"key":"431_CR27","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand, G. et al. Deep learning: a primer for radiologists. Radiographics 37, 2113\u20132131 (2017).","journal-title":"Radiographics"},{"key":"431_CR28","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S. & Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. Artif. Intell. Stat. 1273\u20131282 (2017)."},{"key":"431_CR29","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K. & Doll\u00e1r, P. Focal loss for dense object detection. in IEEE International Conference on Computer Vision 2980\u20132988 (2017).","DOI":"10.1109\/ICCV.2017.324"},{"key":"431_CR30","doi-asserted-by":"crossref","unstructured":"Neubeck, A. & Van Gool, L. Efficient non-maximum suppression. in International Conference on Pattern Recognition, Vol. 3, 850\u2013855 (2006).","DOI":"10.1109\/ICPR.2006.479"},{"key":"431_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-020-00173-2","volume":"4","author":"J Hofmanninger","year":"2020","unstructured":"Hofmanninger, J. et al. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur. Radiol. Exp. 4, 1\u201313 (2020).","journal-title":"Eur. Radiol. Exp."},{"key":"431_CR32","doi-asserted-by":"crossref","unstructured":"Hoiem, D., Chodpathumwan, Y. & Dai, Q. Diagnosing error in object detectors. in European Conference on Computer Vision 340\u2013353 (2012).","DOI":"10.1007\/978-3-642-33712-3_25"}],"updated-by":[{"DOI":"10.1038\/s41746-022-00600-1","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,4,24]],"date-time":"2022-04-24T00:00:00Z","timestamp":1650758400000}}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00431-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00431-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00431-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T18:48:36Z","timestamp":1670093316000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-021-00431-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,29]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["431"],"URL":"https:\/\/doi.org\/10.1038\/s41746-021-00431-6","relation":{"correction":[{"id-type":"doi","id":"10.1038\/s41746-022-00600-1","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,29]]},"assertion":[{"value":"11 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2022","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1038\/s41746-022-00600-1","URL":"https:\/\/doi.org\/10.1038\/s41746-022-00600-1","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Dr. Glocker reports grants from European Commision, during the conduct of the study; personal fees and other from Kheiron Medical Technologies, personal fees and other from HeartFlow, personal fees and other from Microsoft Research, outside the submitted work. Dr. Kainz reports grants from the UK Engineering and Physical Sciences Research Council and Innovate UK, during the conduct of the study; personal fees and other from ThinkSono Ltd, personal fees and other from Ultromics Ltd, personal fees and other from Cydar Medical Ltd, outside the submitted work. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"60"}}