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Purpose: Develop AI-based automated CT image analysis tools for detection,quantification, and tracking of Coronavirus; demonstrate they can differentiatecoronavirus patients from non-patients. Materials and Methods: Multipleinternational datasets, including from Chinese disease-infected areas wereincluded. We present a system that utilizes robust 2D and 3D deep learningmodels, modifying and adapting existing AI models and combining them withclinical understanding. We conducted multiple retrospective experiments toanalyze the performance of the system in the detection of suspected COVID-19thoracic CT features and to evaluate evolution of the disease in each patientover time using a 3D volume review, generating a Corona score. The studyincludes a testing set of 157 international patients (China and U.S). Results:Classification results for Coronavirus vs Non-coronavirus cases per thoracic CTstudies were 0.996 AUC (95%CI: 0.989-1.00) ; on datasets of Chinese control andinfected patients. Possible working point: 98.2% sensitivity, 92.2%specificity. For time analysis of Coronavirus patients, the system outputenables quantitative measurements for smaller opacities (volume, diameter) andvisualization of the larger opacities in a slice-based heat map or a 3D volumedisplay. Our suggested Corona score measures the progression of disease overtime. Conclusion: This initial study, which is currently being expanded to alarger population, demonstrated that rapidly developed AI-based image analysiscan achieve high accuracy in detection of Coronavirus as well as quantificationand tracking of disease burden.

Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic:  Initial Results for Automated Detection & Patient Monitoring using Deep  Learning CT Image Analysis