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


-By Ophir Gozes, Ma’ayan Frid-Adar, Hayit Greenspan, PhD, Patrick D. Browning, MD, Huangqi Zhang, MD, Wenbin Ji, MD, Adam Bernheim, MD, and Eliot Siegel, MD

Abstract


Rapidly developed AI-based automated CT image analysis tools can achieve high accuracy in detection of Coronavirus positive patients as well as quantification of disease burden.

Utilizing the deep-learning image analysis system developed, we achieved classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies of 0.996 AUC (95%CI: 0.989-1.00) on Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity.

For Coronavirus patients the system outputs quantitative opacity measurements and a visualization of the larger opacities in a slice-based “heat map” or a 3D volume display. A suggested “Corona score” measures the progression of patients over time.



The coronavirus infection surprised the world with its rapid spread and has had a major impact on
the lives of billions of people. Non-contrast thoracic CT has been shown to be an effective tool in
detection, quantification and follow-up of disease. Deep learning algorithms can be developed to
assist in analyzing potentially large numbers of thoracic CT exams.

• Purpose:

To develop AI-based automated CT image analysis tools for detection, quantification, and tracking
of Coronavirus and demonstrate that they can differentiate coronavirus patients from those who
do not have the disease.

• 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.



Multiple retrospective experiments conducted 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.


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