1. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.
- Author
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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, and Cai, Wenjia
- Subjects
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COVID-19 , *COMPUTED tomography , *SARS-CoV-2 , *PNEUMONIA , *ARTIFICIAL intelligence , *PROGRESSION-free survival , *PROGNOSIS - 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. • 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 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 NCP versus other common pneumonia and can help to predict the prognosis of COVID-19 patients. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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