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Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196900/ https://www.ncbi.nlm.nih.gov/pubmed/32416069 http://dx.doi.org/10.1016/j.cell.2020.04.045 |
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author | Zhang, Kang Liu, Xiaohong Shen, Jun Li, Zhihuan Sang, Ye Wu, Xingwang Zha, Yunfei Liang, Wenhua Wang, Chengdi Wang, Ke Ye, Linsen Gao, Ming Zhou, Zhongguo Li, Liang Wang, Jin Yang, Zehong Cai, Huimin Xu, Jie Yang, Lei Cai, Wenjia Xu, Wenqin Wu, Shaoxu Zhang, Wei Jiang, Shanping Zheng, Lianghong Zhang, Xuan Wang, Li Lu, Liu Li, Jiaming Yin, Haiping Wang, Winston Li, Oulan Zhang, Charlotte Liang, Liang Wu, Tao Deng, Ruiyun Wei, Kang Zhou, Yong Chen, Ting Lau, Johnson Yiu-Nam Fok, Manson He, Jianxing Lin, Tianxin Li, Weimin Wang, Guangyu |
author_facet | Zhang, Kang Liu, Xiaohong Shen, Jun Li, Zhihuan Sang, Ye Wu, Xingwang Zha, Yunfei Liang, Wenhua Wang, Chengdi Wang, Ke Ye, Linsen Gao, Ming Zhou, Zhongguo Li, Liang Wang, Jin Yang, Zehong Cai, Huimin Xu, Jie Yang, Lei Cai, Wenjia Xu, Wenqin Wu, Shaoxu Zhang, Wei Jiang, Shanping Zheng, Lianghong Zhang, Xuan Wang, Li Lu, Liu Li, Jiaming Yin, Haiping Wang, Winston Li, Oulan Zhang, Charlotte Liang, Liang Wu, Tao Deng, Ruiyun Wei, Kang Zhou, Yong Chen, Ting Lau, Johnson Yiu-Nam Fok, Manson He, Jianxing Lin, Tianxin Li, Weimin Wang, Guangyu |
author_sort | Zhang, Kang |
collection | PubMed |
description | Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19. |
format | Online Article Text |
id | pubmed-7196900 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71969002020-05-04 Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography Zhang, Kang Liu, Xiaohong Shen, Jun Li, Zhihuan Sang, Ye Wu, Xingwang Zha, Yunfei Liang, Wenhua Wang, Chengdi Wang, Ke Ye, Linsen Gao, Ming Zhou, Zhongguo Li, Liang Wang, Jin Yang, Zehong Cai, Huimin Xu, Jie Yang, Lei Cai, Wenjia Xu, Wenqin Wu, Shaoxu Zhang, Wei Jiang, Shanping Zheng, Lianghong Zhang, Xuan Wang, Li Lu, Liu Li, Jiaming Yin, Haiping Wang, Winston Li, Oulan Zhang, Charlotte Liang, Liang Wu, Tao Deng, Ruiyun Wei, Kang Zhou, Yong Chen, Ting Lau, Johnson Yiu-Nam Fok, Manson He, Jianxing Lin, Tianxin Li, Weimin Wang, Guangyu Cell Resource Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19. Elsevier Inc. 2020-06-11 2020-05-04 /pmc/articles/PMC7196900/ /pubmed/32416069 http://dx.doi.org/10.1016/j.cell.2020.04.045 Text en © 2020 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Resource Zhang, Kang Liu, Xiaohong Shen, Jun Li, Zhihuan Sang, Ye Wu, Xingwang Zha, Yunfei Liang, Wenhua Wang, Chengdi Wang, Ke Ye, Linsen Gao, Ming Zhou, Zhongguo Li, Liang Wang, Jin Yang, Zehong Cai, Huimin Xu, Jie Yang, Lei Cai, Wenjia Xu, Wenqin Wu, Shaoxu Zhang, Wei Jiang, Shanping Zheng, Lianghong Zhang, Xuan Wang, Li Lu, Liu Li, Jiaming Yin, Haiping Wang, Winston Li, Oulan Zhang, Charlotte Liang, Liang Wu, Tao Deng, Ruiyun Wei, Kang Zhou, Yong Chen, Ting Lau, Johnson Yiu-Nam Fok, Manson He, Jianxing Lin, Tianxin Li, Weimin Wang, Guangyu Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography |
title | Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography |
title_full | Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography |
title_fullStr | Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography |
title_full_unstemmed | Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography |
title_short | Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography |
title_sort | clinically applicable ai system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography |
topic | Resource |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7196900/ https://www.ncbi.nlm.nih.gov/pubmed/32416069 http://dx.doi.org/10.1016/j.cell.2020.04.045 |
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