Validation of a Deep Learning Model to aid in COVID-19 Detection from Digital Chest Radiographs

Abstract

Abstract Introduction Using artificial intelligence in imaging practice helps ensure study list reprioritization, prompt attention to urgent studies, and reduces the reporting turn-around time. Purpose We tested a deep learning-based artificial intelligence model that can detect COVID-19 pneumonia patterns from digital chest radiographs. Material and Methods The deep learning model was built using the enhanced U-Net architecture with Spatial Attention Gate and Xception Encoder. The model was named DxCOVID and was tested on an external clinical dataset. The dataset included 2247 chest radiographs comprising CXRs from 1046 COVID-19 positive patients (positive on RT-PCR) and 1201 COVID-19 negative patients. Results We compared the performance of the model with three different radiologists by adjusting the model’s sensitivity as per the individual radiologist. The area under the curve (AUC) on the receiver operating characteristic (ROC) of the model was 0.87 [95% CI: 0.85, 0.89]. Conclusion When compared to the performance of three expert readers, DxCOVID matched the output of two of the three readers. Disease-specific deep learning models using current technology are mature enough to match radiologists’ performance and can be a suitable tool to be incorporated into imaging workflows.

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