Computer-aided covid-19 patient screening using chest images (X-Ray and CT scans)
Abstract
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 (<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.quantuscovid19.org">www.quantuscovid19.org</ext-link>). These results merit further evaluation collecting more images. We hope this study will allow us to start such collaborations.
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