{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T15:26:10Z","timestamp":1777994770003,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"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>The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID\u2212) pneumonia and normal controls. We discuss training strategies and differences in performance across 13 international institutions and 8 countries. The inclusion of non-China sites in training significantly improved classification performance with area under the curve (AUCs) and accuracies above 0.8 on most test sites. Furthermore, using available follow-up scans, we investigate methods to track patient disease course and predict prognosis.<\/jats:p>","DOI":"10.1038\/s41746-020-00369-1","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T11:19:10Z","timestamp":1611919150000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2802-0786","authenticated-orcid":false,"given":"Edward H.","family":"Lee","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2009-2059","authenticated-orcid":false,"given":"Jimmy","family":"Zheng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3771-7975","authenticated-orcid":false,"given":"Errol","family":"Colak","sequence":"additional","affiliation":[]},{"given":"Maryam","family":"Mohammadzadeh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5026-5101","authenticated-orcid":false,"given":"Golnaz","family":"Houshmand","sequence":"additional","affiliation":[]},{"given":"Nicholas","family":"Bevins","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9992-5630","authenticated-orcid":false,"given":"Felipe","family":"Kitamura","sequence":"additional","affiliation":[]},{"given":"Emre","family":"Altinmakas","sequence":"additional","affiliation":[]},{"given":"Eduardo Pontes","family":"Reis","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1299-9996","authenticated-orcid":false,"given":"Jae-Kwang","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1662-0995","authenticated-orcid":false,"given":"Chad","family":"Klochko","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2812-6193","authenticated-orcid":false,"given":"Michelle","family":"Han","sequence":"additional","affiliation":[]},{"given":"Sadegh","family":"Moradian","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Mohammadzadeh","sequence":"additional","affiliation":[]},{"given":"Hashem","family":"Sharifian","sequence":"additional","affiliation":[]},{"given":"Hassan","family":"Hashemi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9201-8173","authenticated-orcid":false,"given":"Kavous","family":"Firouznia","sequence":"additional","affiliation":[]},{"given":"Hossien","family":"Ghanaati","sequence":"additional","affiliation":[]},{"given":"Masoumeh","family":"Gity","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2613-0228","authenticated-orcid":false,"given":"Hakan","family":"Do\u011fan","sequence":"additional","affiliation":[]},{"given":"Hojjat","family":"Salehinejad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2634-4851","authenticated-orcid":false,"given":"Henrique","family":"Alves","sequence":"additional","affiliation":[]},{"given":"Jayne","family":"Seekins","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0421-0959","authenticated-orcid":false,"given":"Nitamar","family":"Abdala","sequence":"additional","affiliation":[]},{"given":"\u00c7etin","family":"Atasoy","sequence":"additional","affiliation":[]},{"given":"Hamidreza","family":"Pouraliakbar","sequence":"additional","affiliation":[]},{"given":"Majid","family":"Maleki","sequence":"additional","affiliation":[]},{"given":"S. Simon","family":"Wong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9860-3368","authenticated-orcid":false,"given":"Kristen W.","family":"Yeom","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"369_CR1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1038\/s41586-020-2012-7","volume":"579","author":"P Zhou","year":"2020","unstructured":"Zhou, P. et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270\u2013273 (2020).","journal-title":"Nature"},{"key":"369_CR2","doi-asserted-by":"crossref","unstructured":"Chen, N. et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel Coronavirus Pneumonia in Wuhan, China: a descriptive study. Lancet 395, 507\u2013513 (2020).","DOI":"10.1016\/S0140-6736(20)30211-7"},{"key":"369_CR3","doi-asserted-by":"crossref","unstructured":"Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 Novel Coronavirus-infected Pneumonia in Wuhan, China. JAMA 323, 1061\u20131069 (2020).","DOI":"10.1001\/jama.2020.1585"},{"key":"369_CR4","unstructured":"Li, Q. et al. Early transmission dynamics in Wuhan, China, of novel Coronavirus-Infected Pneumonia. New Engl. J. Med. 382, 1199\u20131207 (2020)."},{"key":"#cr-split#-369_CR5.1","doi-asserted-by":"crossref","unstructured":"Liu, R. et al. Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clinica chimica acta","DOI":"10.1016\/j.cca.2020.03.009"},{"key":"#cr-split#-369_CR5.2","unstructured":"international journal of clinical chemistry 505, 172-175 (2020)."},{"key":"369_CR6","doi-asserted-by":"crossref","unstructured":"Wang, W. et al. Detection of SARS-COV-2 in different types of clinical specimens. JAMA 11, 1843\u20131844 (2020).","DOI":"10.1001\/jama.2020.3786"},{"key":"369_CR7","first-page":"3","volume":"26","author":"J Pan","year":"2020","unstructured":"Pan, J. et al. Potential rapid diagnostics, vaccine and therapeutics for 2019 novel Coronavirus (2019-ncov): a systemic review. J. Clin. Med. 26, 3 (2020).","journal-title":"J. Clin. Med."},{"key":"369_CR8","doi-asserted-by":"crossref","unstructured":"Pulia, M. S., O'Brien, T. P., Hou, P. C., Schuman, A. & Sambursky, R. Multi-tiered screening and diagnosis strategy for COVID-19: a model for sustainable testing capacity in response to pandemic. Ann. Med. 52, 207\u2013214 (2020).","DOI":"10.1080\/07853890.2020.1763449"},{"key":"369_CR9","doi-asserted-by":"crossref","unstructured":"Omer, S. B., Malani, P. & del Rio, C. The COVID-19 pandemic in the us: a clinical update. JAMA 323, 1767\u20131768 (2020).","DOI":"10.1001\/jama.2020.5788"},{"key":"369_CR10","first-page":"394","volume":"80","author":"YH Xu","year":"2020","unstructured":"Xu, Y. H. et al. Clinical and computed tomographic imaging features of novel Coronavirus Pneumonia caused by SARS-COV-2. J. Infect. Dis. 80, 394\u2013400 (2020).","journal-title":"J. Infect. Dis."},{"key":"369_CR11","doi-asserted-by":"crossref","unstructured":"Xie, Z. et al. Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing. Radiology 296, E41\u2013E45 (2020).","DOI":"10.1148\/radiol.2020200343"},{"key":"369_CR12","doi-asserted-by":"crossref","unstructured":"Ai, T. et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 296, E32\u2013E40 (2020).","DOI":"10.1148\/radiol.2020200642"},{"key":"369_CR13","doi-asserted-by":"crossref","unstructured":"Yang, W. et al. The role of imaging in 2019 novel Coronavirus Pneumonia (COVID-19). Eur. Radiol. 30, 4874\u20134882 (2020).","DOI":"10.1007\/s00330-020-06827-4"},{"key":"369_CR14","doi-asserted-by":"crossref","unstructured":"Liu, K. C. et al. CT manifestations of Coronavirus disease-2019: a retrospective analysis of 73 cases by disease severity. Eur. J. Radiol. 126, 108941 (2020).","DOI":"10.1016\/j.ejrad.2020.108941"},{"key":"369_CR15","doi-asserted-by":"crossref","unstructured":"Li, K. et al. The clinical and chest CT features associated with severe and critical COVID-19 Pneumonia. Invest. Radiol. 55, 327\u2013331 (2020).","DOI":"10.1097\/RLI.0000000000000672"},{"key":"369_CR16","doi-asserted-by":"crossref","unstructured":"Li, L. et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296, E65\u2013E71 (2020).","DOI":"10.1148\/radiol.2020200905"},{"key":"369_CR17","doi-asserted-by":"crossref","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.e1\u2013e11 (2020).","DOI":"10.1016\/j.cell.2020.04.045"},{"key":"369_CR18","doi-asserted-by":"crossref","unstructured":"Harmon, S. A. et al. Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nat. Commun. 11, 1\u20137 (2020).","DOI":"10.1038\/s41467-020-17971-2"},{"key":"369_CR19","doi-asserted-by":"crossref","unstructured":"Huang, L. et al. Serial quantitative chest ct assessment of covid-19: Deep-learning approach. Radiol. Cardiothor. Imaging 2, e200075 (2020).","DOI":"10.1148\/ryct.2020200075"},{"key":"369_CR20","doi-asserted-by":"crossref","unstructured":"Selvaraju, R. et al. Grad-cam: visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision. pp. 618\u2013626 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.74"},{"key":"369_CR21","doi-asserted-by":"crossref","unstructured":"Wu, X. et al. Deep learning-based multi-view fusion model for screening 2019 novel Coronavirus Pneumonia: a multicenter study. Eur. J. Radiol. 128, 109041 (2020).","DOI":"10.1016\/j.ejrad.2020.109041"},{"key":"369_CR22","doi-asserted-by":"crossref","unstructured":"Li, Z. et al. From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of covid-19 on thick-section CT scans. Eur. Radiol. 30, 6828\u20136837 (2020).","DOI":"10.1007\/s00330-020-07042-x"},{"key":"369_CR23","doi-asserted-by":"crossref","unstructured":"Wang, S. et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur. Respiratory J. 56, 2000775 (2020).","DOI":"10.1183\/13993003.00775-2020"},{"key":"369_CR24","doi-asserted-by":"publisher","first-page":"E18","DOI":"10.1148\/radiol.2020202439","volume":"298","author":"N Lessmann","year":"2021","unstructured":"Lessmann, N. et al. Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence. Radiology 298, E18\u2013E28 (2021).","journal-title":"Radiology"},{"key":"369_CR25","doi-asserted-by":"crossref","unstructured":"Pu, J. et al. Automated quantification of covid-19 severity and progression using chest CT images. Eur. Radiol. 31, 436\u2013446 (2020).","DOI":"10.1007\/s00330-020-07156-2"},{"key":"369_CR26","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.2020200230","volume":"295","author":"M Chung","year":"2020","unstructured":"Chung, M. et al. CT imaging features of 2019 novel Coronavirus (2019-nCOV). Radiology 295, 202\u2013207 (2020).","journal-title":"Radiology"},{"key":"369_CR27","doi-asserted-by":"crossref","unstructured":"Saleh, S. et al. Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients. Am. J. Roentgenol. 14, 1\u20137 (2020).","DOI":"10.2214\/AJR.20.23034"},{"key":"369_CR28","doi-asserted-by":"crossref","unstructured":"Song, F. et al. Emerging Coronavirus 2019-nCOV pneumonia. Radiology 295, 210\u2013217 (2020).","DOI":"10.1148\/radiol.2020200274"},{"key":"369_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y. et al. Temporal changes of CT findings in 90 patients with Covid-19 Pneumonia: a longitudinal study. Radiology 296, E55\u2013E64 (2020).","DOI":"10.1148\/radiol.2020200843"},{"key":"369_CR30","doi-asserted-by":"crossref","unstructured":"Pan, F. et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology 295, 715\u2013721 (2020).","DOI":"10.1148\/radiol.2020200370"},{"key":"369_CR31","doi-asserted-by":"crossref","unstructured":"Song, J. et al. End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT. Eur. J. Nucl. Med. Mol. Imaging 47, 2516\u20132524 (2020).","DOI":"10.1007\/s00259-020-04929-1"},{"key":"369_CR32","doi-asserted-by":"crossref","unstructured":"Singh, D. et al. Classification of covid-19 patients from chest ct images using multi-objective differential evolution-based convolutional neural networks. Eur. J. Clin. Microbiol. Infect. Dis.: official publication of the European Society of Clinical Microbiology 39, 1379\u20131389 (2020).","DOI":"10.1007\/s10096-020-03901-z"},{"key":"369_CR33","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Gianchandani, N., Singh, D., Kumar, V. & Kaur, M. Classification of the covid-19 infected patients using densenet201 based deep transfer learning. J. Biomol. Structure Dynam. 1\u20138. Advance online publication (2020).","DOI":"10.1080\/07391102.2020.1788642"},{"key":"369_CR34","doi-asserted-by":"crossref","unstructured":"Morozov, S. P. et al. MosMedData: chest CT scans with COVID-19 related findings dataset. (2020).","DOI":"10.1101\/2020.05.20.20100362"},{"key":"369_CR35","unstructured":"Tsang, S. H. Review: GoogLeNet (Inception v1)\u2013Winner of ILSVRC 2014 (Image Classification) (2018)."},{"key":"369_CR36","doi-asserted-by":"crossref","unstructured":"Carreira, J. & Zisserman, A. Quo vadis, action recognition? a new model and the kinetics dataset. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 6299\u20136308 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.502"},{"key":"369_CR37","unstructured":"Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations (Bengio, Y. and LeCun, Y. eds) (ICLR, San Diego, 2015)."},{"key":"369_CR38","doi-asserted-by":"crossref","unstructured":"Davidson-Pilon, C. Lifelines: survival analysis in Python. J Open Source Software 1317 (2019).","DOI":"10.21105\/joss.01317"},{"key":"369_CR39","unstructured":"COVID-19 RICORD - RSNA. RSNA International COVID-19 open radiology database. https:\/\/www.rsna.org\/en\/covid-19\/COVID-19-RICORD (2020)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00369-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00369-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00369-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T19:11:04Z","timestamp":1670094664000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00369-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["369"],"URL":"https:\/\/doi.org\/10.1038\/s41746-020-00369-1","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,29]]},"assertion":[{"value":"15 May 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11"}}