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Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
BACKGROUND: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. PURPOSE: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. M...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Radiological Society of North America
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233473/ https://www.ncbi.nlm.nih.gov/pubmed/32191588 http://dx.doi.org/10.1148/radiol.2020200905 |
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author | Li, Lin Qin, Lixin Xu, Zeguo Yin, Youbing Wang, Xin Kong, Bin Bai, Junjie Lu, Yi Fang, Zhenghan Song, Qi Cao, Kunlin Liu, Daliang Wang, Guisheng Xu, Qizhong Fang, Xisheng Zhang, Shiqin Xia, Juan Xia, Jun |
author_facet | Li, Lin Qin, Lixin Xu, Zeguo Yin, Youbing Wang, Xin Kong, Bin Bai, Junjie Lu, Yi Fang, Zhenghan Song, Qi Cao, Kunlin Liu, Daliang Wang, Guisheng Xu, Qizhong Fang, Xisheng Zhang, Shiqin Xia, Juan Xia, Jun |
author_sort | Li, Lin |
collection | PubMed |
description | BACKGROUND: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. PURPOSE: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. MATERIALS AND METHODS: In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. RESULTS: The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases. |
format | Online Article Text |
id | pubmed-7233473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Radiological Society of North America |
record_format | MEDLINE/PubMed |
spelling | pubmed-72334732020-06-02 Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT Li, Lin Qin, Lixin Xu, Zeguo Yin, Youbing Wang, Xin Kong, Bin Bai, Junjie Lu, Yi Fang, Zhenghan Song, Qi Cao, Kunlin Liu, Daliang Wang, Guisheng Xu, Qizhong Fang, Xisheng Zhang, Shiqin Xia, Juan Xia, Jun Radiology Original Research BACKGROUND: Coronavirus disease has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. PURPOSE: To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances. MATERIALS AND METHODS: In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19. Community acquired pneumonia (CAP) and other non-pneumonia CT exams were included to test the robustness of the model. The datasets were collected from 6 hospitals between August 2016 and February 2020. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. RESULTS: The collected dataset consisted of 4356 chest CT exams from 3,322 patients. The average age is 49±15 years and there were slightly more male patients than female (1838 vs 1484; p-value=0.29). The per-exam sensitivity and specificity for detecting COVID-19 in the independent test set was 114 of 127 (90% [95% CI: 83%, 94%]) and 294 of 307 (96% [95% CI: 93%, 98%]), respectively, with an AUC of 0.96 (p-value<0.001). The per-exam sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175) and 92% (239 of 259), respectively, with an AUC of 0.95 (95% CI: 0.93, 0.97). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases. Radiological Society of North America 2020-03-19 /pmc/articles/PMC7233473/ /pubmed/32191588 http://dx.doi.org/10.1148/radiol.2020200905 Text en 2020 by the Radiological Society of North America, Inc. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | Original Research Li, Lin Qin, Lixin Xu, Zeguo Yin, Youbing Wang, Xin Kong, Bin Bai, Junjie Lu, Yi Fang, Zhenghan Song, Qi Cao, Kunlin Liu, Daliang Wang, Guisheng Xu, Qizhong Fang, Xisheng Zhang, Shiqin Xia, Juan Xia, Jun Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT |
title | Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT |
title_full | Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT |
title_fullStr | Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT |
title_full_unstemmed | Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT |
title_short | Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT |
title_sort | artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233473/ https://www.ncbi.nlm.nih.gov/pubmed/32191588 http://dx.doi.org/10.1148/radiol.2020200905 |
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