Cited by 917COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

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Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

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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·2020·Cell

DOI 10.1016/j.cell.2020.04.045PMC PMC7196900

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Abstract

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.

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Authors: Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., Ye, L., Gao, M., Zhou, Z., Li, L., Wang, J., Yang, Z., Cai, H., Xu, J., Yang, L., Cai, W., Xu, W., Wu, S., Zhang, W., Jiang, S., Zheng, L., Zhang, X., Wang, L., Lu, L., Li, J., Yin, H., Wang, W., Li, O., Zhang, C., Liang, L., Wu, T., Deng, R., Wei, K., Zhou, Y., Chen, T., Lau, J. Y.-N., Fok, M., He, J., Lin, T., Li, W., & Wang, G.
Affiliations: Macau University of Science and Technology (MO)
Tsinghua University (CN)
Sun Yat-sen University (CN)
Sichuan University (CN)
China Three Gorges University (CN)
First Affiliated Hospital of Anhui Medical University (CN)
Wuhan University (CN)
First Affiliated Hospital of Guangzhou Medical University (CN)
Guangzhou Regenerative Medicine and Health Guangdong Laboratory (CN)
First People's Hospital of Yunnan Province (CN)
Hong Kong Polytechnic University (HK)
Beijing National Research Center for Information Science and Technology (CN)
Corresponding author: Zhang, Kang
He, Jianxing
Lin, Tianxin
Li, Weimin
Wang, Guangyu
ORCID: 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
Xu, Jie
Yang, Lei
Cai, Wenjia
Wu, Shaoxu
Zhang, Wei
Jiang, Shanping
Zheng, Lianghong
Zhang, Xuan
Wang, Li
Li, Jiaming
Wang, Winston
Zhang, Charlotte
Liang, Liang
Wu, Tao
Zhou, Yong
Chen, Ting
He, Jianxing
Lin, Tianxin
Li, Weimin
Wang, Guangyu
Venue: Cell
Publisher: Elsevier BV
Published: 2020-06
Volume: 181
Issue: 6
Pages: 1423-1433.e11
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2020.04.045
PMC: PMC7196900
OpenAlex: W3020653337
Cited by: 917
References: 46
Citations by year: 2026: 43 · 2025: 69 · 2024: 106 · 2023: 138 · 2022: 190 · 2021: 273 · 2020: 97
FWCI: 84.99
Type: journal article
Language: en
Update notices: erratum (2020-09-03)
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 375 d)
Locations: Cell (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Macau University of Science and Technology
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: COVID-19 pneumonia, SARS-CoV-2, computed tomography, artificial intelligence, pneumonia diagnosis, differential diagnosis, lesion quantification, prognosis, clinical biomarkers, radiology, clinical decision support
MeSH terms: Radiologists, COVID-19, Artificial Intelligence, China, Humans, Lung, Models, Biological, Pilot Projects, Pneumonia, Viral, Prognosis, Respiratory Insufficiency, Tomography, X-Ray Computed, Cohort Studies, Coronavirus Infections, Pandemics, Datasets as Topic

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Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography

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