PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR) MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives) The CXR were independently reviewed by 3 radiologists and using the DL algorithm Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC) RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male) The DL algorithm achieved an AUC of 0 81, sensitivity of 0 85, and specificity of 0 72 in detecting COVID-19 using RT-PCR as the reference standard On subgroup analyses, the model achieved an AUC of 0 79, sensitivity of 0 80, and specificity of 0 74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0 87, sensitivity of 0 85, and specificity of 0 81 in distinguishing COVID-19 from other forms of pneumonia The algorithm significantly outperforms human readers (P&lt;0 001 using DeLong test) with higher sensitivity (P=0 01 using McNemar test) CONCLUSIONS: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems
Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs