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