Evaluation of multiple open-source deep learning models for detecting and grading COVID-19 on chest radiographs

Purpose: Chest x-rays are complex to report accurately. Viral pneumonia is often subtle in its radiological appearance. In the context of the COVID-19 pandemic, rapid triage of cases and exclusion of other pathologies with artificial intelligence (AI) can assist over-stretched radiology departments. We aim to validate three open-source AI models on an external test set. Approach: We tested three open-source deep learning models, COVID-Net, COVIDNet-S-GEO, and CheXNet for their ability to detect COVID-19 pneumonia and to determine its severity using 129 chest x-rays from two different vendors Phillips and Agfa. Results: All three models detected COVID-19 pneumonia (AUCs from 0.666 to 0.778). Only the COVID Net-S-GEO and CheXNet models performed well on severity scoring (Pearson's $$ 0.927 and 0.833, respectively); COVID-Net only performed well at either task on images taken with a Philips machine (AUC 0.735) and not an Agfa machine (AUC 0.598). Conclusions: Chest x-ray triage using existing machine learning models for COVID-19 pneumonia can be successfully implemented using open-source AI models. Evaluation of the model using local x-ray machines and protocols is highly recommended before implementation to avoid vendor or protocol dependent bias.