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

Authors :
Liang Liang
Yunfei Zha
Jiaming Li
Ruiyun Deng
Haiping Yin
Winston Wang
Charlotte Zhang
Jun Shen
Huimin Cai
Shaoxu Wu
Ming Gao
Li Wang
Liu Lu
Guangyu Wang
Jie Xu
Lianghong Zheng
Xuan Zhang
Liang Li
Zehong Yang
Oulan Li
Xiaohong Liu
Yong Zhou
Tianxin Lin
Linsen Ye
Ye Sang
Kang Zhang
Jianxing He
Wenqin Xu
Lei Yang
Zhongguo Zhou
Xingwang Wu
Tao Wu
Wenhua Liang
Zhihuan Li
Manson Fok
Weimin Li
Wei Zhang
Ke Wang
Wenjia Cai
Johnson Y.N. Lau
Jin Wang
Chengdi Wang
Kang Wei
Shanping Jiang
Ting Chen
Source :
Cell
Publication Year :
2020
Publisher :
Elsevier BV, 2020.

Abstract

Summary 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 4,154 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.<br />Highlights • AI system that can diagnose COVID-19 pneumonia using CT scans • Prediction of progression to critical illness • Potential to improve performance of junior radiologists to the senior level • Can assist evaluation of drug treatment effects with CT quantification<br />Zhang et al. present an AI-based system, based on hundreds of thousands of human lung CT scan images, that can aid in distinguishing patients with pneumonia caused by SARS-CoV-2 versus other viral infections and can help to predict the prognosis of COVID-19 patients.

Details

ISSN :
00928674
Volume :
181
Database :
OpenAIRE
Journal :
Cell
Accession number :
edsair.doi.dedup.....208c27f9d5d80cb9850f54cc8f6ffeaf