Computer-aided covid-19 patient screening using chest images (X-Ray and CT scans)

Burgos-Artizzu, X. P.

OBJECTIVES To evaluate the performance of Artificial Intelligence (AI) methods to detect covid -19 from chest images (X-Ray and CT scans). METHODS Chest CT scans and X-Ray images collected from different centers and institutions were downloaded and combined together. Images were separated by patient and 66% of the patients were used to develop and train AI image-based classifiers. Then, the AI automated classifiers were evaluated on a separate set of patients (the remaining 33% patients). RESULTS (Chest X-Ray) Five different data sources were combined for a total of N=9,841 patients (1,733 with covid-19, 810 with bacterial tuberculosis and 7,298 healthy patients). The test sample size was N=3,528 patients. The best AI method reached an Area Under the Curve (AUC) for covid-19 detection of 99%, with a detection rate of 96.4% at 1.0% false positive rate. RESULTS (Chest CT scans) Two different data sources were combined for a total of N=363 patients (191 having covid-19 and 172 healthy patients). The test sample size was N=121 patients. The best AI method reached an AUC for covid-19 detection of 90.9%, with a detection rate of 90.6% at 24.6% false positive rate. CONCLUSIONS Computer aided automatic covid-19 detection from chest X-ray images showed promising results to be used as screening tool during the covid-19 outbreak. The developed method may help to manage patients better in case access to PCR testing is not possible or to detect patients with symptoms missed in a first round of PCR testing. The method will be made available online (www.quantuscovid19.org). These results merit further evaluation collecting more images. We hope this study will allow us to start such collaborations.