ABSTRACT
Introduction/Hypothesis: To validate an AI algorithm for rapid detection of COVID-19 pulmonary complications and prediction of negative 30-day outcomes in COVID-suspicious patients. Methods: We included 2000 chest X-rays (CXR) from patients who had a COVID-19 RT-PCR test within 14 days of the CXR. A deep learning CNN (AI-RAD Companion, Siemens) previously trained for non-COVID pneumonia was used to analyze the CXRs. A total of 1544 CXRs were first used to train the AI with COVID cases. Then, a randomized modified internal holdout of 456 patients (236 positive, 220 negative) were used as test cohort. AI results detect the presence of COVID-19 lung disease (CLD) and also report a 1 to 10 AI severity score. Positive RT-PCR within 14 days of the CXR was used as the ground-truth for COVID diagnosis. Radiologic assessment by three cardiothoracic radiologists was used to detect the presence of CLD and generate a 1 to 10 expert severity score. All-cause mortality within 30 days of the CXR was recorded. Receiver-operating characteristic (ROC) curve analysis was performed and the area under the curve (AUC) was reported. Concordance metrics included intraclass correlation coefficient (ICC) for comparison of AI and expert results. Results: In COVID+ patients, AI demonstrated a sensitivity of 99% (205/207) , specificity of 62% (18/29), PPV of 95% (205/216), and NPV of 90% (18/20), for the detection of CLD. Amongst COVID+ patients, the AI severity score had excellent agreement with the expert severity score for lung involvement (ICC=0.89, 95% CI 0.86-0.92). There were 70 deaths in the test cohort (15.3%). The AI severity score had an excellent ability to predict all-cause mortality (AUC=0.832 vs expert AUC=0.844, p >0.05). Conclusions: This CXR AI tool had an excellent sensitivity for detection of COVID-19 lung disease in PCR-positive patients and excellent correlation with expert analysis. AI severity score was able to strongly predict 30-day patient all-cause mortality.