Stepwise Diagnosis and Dynamic Change Prediction AI System for COVID 19

Background: As the COVID-19 pandemic continues to spread worldwide, there is still no accurate rapid diagnostic test for COVID-19, or monitoring tool for patient’s clinical course. An artificial-intelligence system (CoviDet) was therefore developed and applied on chest computed tomography (CT), to evaluate its application for rapid diagnosis and potential monitoring of COVID-19 patients. Methods: 1,201,074 CT slices from 2527 patients were grouped into 3 main groups: COVID-19 positive group, non-COVID-19 viral pneumonia group and control group. They were used to train and validate CoviDet novel stepwise diagnostic algorithm to diagnose COVID-19, with or without clinical data. A subset of COVID-19 patients with more than 3 consecutive CT images were selected for training and validation of the auto-segmentation and monitoring algorithm. Findings: CoviDet outperforms radiologists, and can diagnose COVID-19 based on CT alone with sensitivity of 0.93, specificity of 0.95 and AUC of 0.98 (95%CI 0.97-0.99; P<0.001). Its auto-segmentation analyses co-related well with those by radiologists, with a Dice's coefficient of 0.77. Its can generate a predictive curve of patient’s clinical course if serial CT assessments are available. Interpretation: CoviDet is a useful aid for radiologist to diagnose COVID-19 rapidly and accurately based on CT, with or without clinical data. It can produce an objective predictive curve of patient’s clinical course for easy visualisation. In principle, CoviDet could be used on a web-based platform with built auto masking of privacy data, whereby clinicians can upload anonymised CT for COVID-19 analysis without sensitive clinical information, from anywhere in the world. Funding Statement: This study is supported by High-level university construction project of Guangzhou medical university (Grant No. 20182737, 201721007, 201715907, 2017160107); the Guangdong high level hospital construction reaching peak plan. Declaration of Interests: Qiang Du is the Founder and CEO of Beijing XiaoBaiShiJi Network Technical Co. Ltd. All other authors declare no competing interests. Ethics Approval Statement: This study is approved by the ethics committee of the First Affiliated Hospital of Guangzhou Medical University.