Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT
Abstract 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 neur...
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Nature Portfolio
2021
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oai:doaj.org-article:b8cdd6ddc1174a5a86416c48a44db3cc2021-12-02T14:18:07ZDeep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT10.1038/s41746-020-00369-12398-6352https://doaj.org/article/b8cdd6ddc1174a5a86416c48a44db3cc2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00369-1https://doaj.org/toc/2398-6352Abstract 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−) 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.Edward H. LeeJimmy ZhengErrol ColakMaryam MohammadzadehGolnaz HoushmandNicholas BevinsFelipe KitamuraEmre AltinmakasEduardo Pontes ReisJae-Kwang KimChad KlochkoMichelle HanSadegh MoradianAli MohammadzadehHashem SharifianHassan HashemiKavous FirouzniaHossien GhanaatiMasoumeh GityHakan DoğanHojjat SalehinejadHenrique AlvesJayne SeekinsNitamar AbdalaÇetin AtasoyHamidreza PouraliakbarMajid MalekiS. Simon WongKristen W. YeomNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-11 (2021) |
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DOAJ |
| collection |
DOAJ |
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EN |
| topic |
Computer applications to medicine. Medical informatics R858-859.7 |
| spellingShingle |
Computer applications to medicine. Medical informatics R858-859.7 Edward H. Lee Jimmy Zheng Errol Colak Maryam Mohammadzadeh Golnaz Houshmand Nicholas Bevins Felipe Kitamura Emre Altinmakas Eduardo Pontes Reis Jae-Kwang Kim Chad Klochko Michelle Han Sadegh Moradian Ali Mohammadzadeh Hashem Sharifian Hassan Hashemi Kavous Firouznia Hossien Ghanaati Masoumeh Gity Hakan Doğan Hojjat Salehinejad Henrique Alves Jayne Seekins Nitamar Abdala Çetin Atasoy Hamidreza Pouraliakbar Majid Maleki S. Simon Wong Kristen W. Yeom Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT |
| description |
Abstract 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−) 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. |
| format |
article |
| author |
Edward H. Lee Jimmy Zheng Errol Colak Maryam Mohammadzadeh Golnaz Houshmand Nicholas Bevins Felipe Kitamura Emre Altinmakas Eduardo Pontes Reis Jae-Kwang Kim Chad Klochko Michelle Han Sadegh Moradian Ali Mohammadzadeh Hashem Sharifian Hassan Hashemi Kavous Firouznia Hossien Ghanaati Masoumeh Gity Hakan Doğan Hojjat Salehinejad Henrique Alves Jayne Seekins Nitamar Abdala Çetin Atasoy Hamidreza Pouraliakbar Majid Maleki S. Simon Wong Kristen W. Yeom |
| author_facet |
Edward H. Lee Jimmy Zheng Errol Colak Maryam Mohammadzadeh Golnaz Houshmand Nicholas Bevins Felipe Kitamura Emre Altinmakas Eduardo Pontes Reis Jae-Kwang Kim Chad Klochko Michelle Han Sadegh Moradian Ali Mohammadzadeh Hashem Sharifian Hassan Hashemi Kavous Firouznia Hossien Ghanaati Masoumeh Gity Hakan Doğan Hojjat Salehinejad Henrique Alves Jayne Seekins Nitamar Abdala Çetin Atasoy Hamidreza Pouraliakbar Majid Maleki S. Simon Wong Kristen W. Yeom |
| author_sort |
Edward H. Lee |
| title |
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT |
| title_short |
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT |
| title_full |
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT |
| title_fullStr |
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT |
| title_full_unstemmed |
Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT |
| title_sort |
deep covid detect: an international experience on covid-19 lung detection and prognosis using chest ct |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/b8cdd6ddc1174a5a86416c48a44db3cc |
| work_keys_str_mv |
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