Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs.

Background: Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before.

Objective: To evaluate the diagnostic performance of a doctor-trained DL model (Svita_DL8) to screen for COVID-19 on CXR, and to compare the performance of the DL model with that of expert radiologists.

Materials And Methods: We used a pre-trained convolutional neural network to develop a publicly available online DL model to evaluate CXR examinations saved in .