Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

TLDR

Early detection of COVID‑19 using chest CT can enable timely treatment and help control disease spread. The study proposes an AI system for rapid COVID‑19 detection and performs extensive statistical analysis of CT scans. The AI system was trained and evaluated on a large dataset of over 10,000 CT volumes from COVID‑19, influenza, CAP, and non‑pneumonia cases, compared CT to CXR performance, and its deep network outputs were interpreted to relate to CT findings. The system achieved a 97.81% AUC on a 3,199‑scan test cohort and 92.99%–93.25% on CC‑CCII and MosMedData, outperforming five radiologists in challenging tasks at a speed two orders of magnitude faster. Code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19.

Abstract

Abstract Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .