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Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia
BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep le...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867927/ https://www.ncbi.nlm.nih.gov/pubmed/33569413 http://dx.doi.org/10.21037/atm-20-5328 |
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author | Zhou, Min Yang, Dexiang Chen, Yong Xu, Yanping Xu, Jin-Fu Jie, Zhijun Yao, Weiwu Jin, Xiaoyan Pan, Zilai Tan, Jingwen Wang, Lan Xia, Yihan Zou, Longkuan Xu, Xin Wei, Jingqi Guan, Mingxin Yan, Fuhua Feng, Jianxing Zhang, Huan Qu, Jieming |
author_facet | Zhou, Min Yang, Dexiang Chen, Yong Xu, Yanping Xu, Jin-Fu Jie, Zhijun Yao, Weiwu Jin, Xiaoyan Pan, Zilai Tan, Jingwen Wang, Lan Xia, Yihan Zou, Longkuan Xu, Xin Wei, Jingqi Guan, Mingxin Yan, Fuhua Feng, Jianxing Zhang, Huan Qu, Jieming |
author_sort | Zhou, Min |
collection | PubMed |
description | BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). METHODS: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5–63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0–78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. RESULTS: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <−500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5–10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. CONCLUSIONS: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission. |
format | Online Article Text |
id | pubmed-7867927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-78679272021-02-09 Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia Zhou, Min Yang, Dexiang Chen, Yong Xu, Yanping Xu, Jin-Fu Jie, Zhijun Yao, Weiwu Jin, Xiaoyan Pan, Zilai Tan, Jingwen Wang, Lan Xia, Yihan Zou, Longkuan Xu, Xin Wei, Jingqi Guan, Mingxin Yan, Fuhua Feng, Jianxing Zhang, Huan Qu, Jieming Ann Transl Med Original Article BACKGROUND: Chest computed tomography (CT) has been found to have high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at the early stage, giving it an advantage over nucleic acid detection during the current pandemic. In this study, we aimed to develop and validate an integrated deep learning framework on chest CT images for the automatic detection of NCP, focusing particularly on differentiating NCP from influenza pneumonia (IP). METHODS: A total of 148 confirmed NCP patients [80 male; median age, 51.5 years; interquartile range (IQR), 42.5–63.0 years] treated in 4 NCP designated hospitals between January 11, 2020 and February 23, 2020 were retrospectively enrolled as a training cohort, along with 194 confirmed IP patients (112 males; median age, 65.0 years; IQR, 55.0–78.0 years) treated in 5 hospitals from May 2015 to February 2020. An external validation set comprising 57 NCP patients and 50 IP patients from 8 hospitals was also enrolled. Two deep learning schemes (the Trinary scheme and the Plain scheme) were developed and compared using receiver operating characteristic (ROC) curves. RESULTS: Of the NCP lesions, 96.6% were >1 cm and 76.8% were of a density <−500 Hu, indicating them to have less consolidation than IP lesions, which had nodules ranging from 5–10 mm. The Trinary scheme accurately distinguished NCP from IP lesions, with an area under the curve (AUC) of 0.93. For patient-level classification in the external validation set, the Trinary scheme outperformed the Plain scheme (AUC: 0.87 vs. 0.71) and achieved human specialist-level performance. CONCLUSIONS: Our study has potentially provided an accurate tool on chest CT for early diagnosis of NCP with high transferability and showed high efficiency in differentiating between NCP and IP; these findings could help to reduce misdiagnosis and contain the pandemic transmission. AME Publishing Company 2021-01 /pmc/articles/PMC7867927/ /pubmed/33569413 http://dx.doi.org/10.21037/atm-20-5328 Text en 2021 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Zhou, Min Yang, Dexiang Chen, Yong Xu, Yanping Xu, Jin-Fu Jie, Zhijun Yao, Weiwu Jin, Xiaoyan Pan, Zilai Tan, Jingwen Wang, Lan Xia, Yihan Zou, Longkuan Xu, Xin Wei, Jingqi Guan, Mingxin Yan, Fuhua Feng, Jianxing Zhang, Huan Qu, Jieming Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
title | Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
title_full | Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
title_fullStr | Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
title_full_unstemmed | Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
title_short | Deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
title_sort | deep learning for differentiating novel coronavirus pneumonia and influenza pneumonia |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867927/ https://www.ncbi.nlm.nih.gov/pubmed/33569413 http://dx.doi.org/10.21037/atm-20-5328 |
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