Artificial Intelligence Assessment of Chest Radiographs for COVID-19

Abstract

Background: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Chest computed tomography (CT) is reported to have high sensitivity; however, given the limited availability of chest CT during a pandemic, the assessment of more readily available imaging, such as chest radiographs, augmented by artificial intelligence may substitute for the detection of the features of coronavirus disease 2019 (COVID-19) pneumonia. Methods: We trained a deep convolutional neural network to detect SARS-CoV-2 pneumonia using publicly available chest radiography imaging data including 8,851 normal, 6,045 pneumonia, and 200 COVID-19 pneumonia radiographs. The entire cohort was divided into training (n = 13,586) and test groups (n = 1510). We assessed the accuracy of prediction with independent external data. Results: The sensitivity and positive predictive values of the assessment by artificial intelligence were 96.8% and 90.9%, respectively. In the first external validation of 204 chest radiographs among 107 patients with confirmed COVID-19, the artificial intelligence algorithm correctly identified 174 (85%) chest radiographs as COVID-19 pneumonia among 97 (91%) patients. In the second external validation with 50 immunocompromised patients with leukemia, the higher probability of the artificial intelligence assessment for COVID-19 was correlated with suggestive features of COVID-19, while the normal chest radiographs were closely correlated with the likelihood of normal chest radiographs by the artificial intelligence prediction. Conclusions: The assessment method by artificial intelligence identified suspicious lung lesions on chest radiographs. This novel approach can identify patients for confirmatory chest CT before the progression of COVID-19 pneumonia.