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Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography
Open accessAbstract
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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“The implementation of AI in healthcare sectors [47, 146] have created an urge amongst researchers to pursue innovative methods to develop e-healthcare systems.”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [47]
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“Existing AI models to date are mostly designed for lung lesion segmentation using convolutional neural networks (CNNs)15,16, requiring dense pixel-wise labels through time-consuming, labor-intensive manual annotation from experts who are scarce during the crisis.”
From Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study (2021) · cites this paper as [15]
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Metadata
- 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)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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