Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis
Ophir Gozes, Maayan Frid-Adar, Hayit Greenspan, Patrick D. Browning,, Huangqi Zhang, Wenbin Ji, Adam Bernheim, Eliot Siegel

TL;DR
This study presents a rapid AI system using deep learning for accurate detection, quantification, and monitoring of COVID-19 in CT images, demonstrating high accuracy and potential for clinical application.
Contribution
It introduces a novel AI-based CT analysis system that combines 2D and 3D deep learning models with clinical insights for COVID-19 detection and disease tracking.
Findings
Achieved 0.996 AUC in classifying COVID-19 from non-COVID cases.
System provides quantitative measurements of disease progression.
High sensitivity and specificity in initial testing.
Abstract
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…
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Taxonomy
TopicsCOVID-19 diagnosis using AI · Radiomics and Machine Learning in Medical Imaging · Advanced X-ray and CT Imaging
