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. 2021 Feb 18;11:4145. doi: 10.1038/s41598-021-83424-5

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Flow diagram illustrating the division of model development data into training, validation, initial test, and secondary test on the secondary test dataset. The model development cohort consisted of 1197 COVID-19 patients and 1,081 non-COVID-19 patients. The secondary test cohort enrolled 233 COVID-19 patients and 289 non-COVID-19 patients. 285 patients were excluded due to ≥ 2 weeks interval of the first CT scan to the first positive nucleic acid test (244 patients), and severe artifacts (41 patients). The model development cohort was then divided into a training dataset (1316 patients), a validation dataset (305 patients), and an initial test dataset (372 patients) to train and fine-tune COVIDNet. A secondary test was then performed to test the model generalizability by comparing the diagnostic performance of COVIDNet and 8 different expert radiologists on the secondary test dataset. *COVID-19 radiologists: radiologists working at the COVID-19 designated hospitals; §non-COVID-19 radiologists: radiologists not working at the COVID-19 designated hospitals.