Artificial Intelligence to Accelerate COVID-19 Identification from Chest X-rays

Abstract

Importance: Clinical signs and symptoms for COVID-19 remain the mainstay of early diagnosis and initial management in the emergency department (ED) and inpatient setting at many hospitals due to de- lays in obtaining results of PCR testing and limitations in access to rapid antigen testing. The majority of many patients with COVID- 19 will present with respiratory symptoms necessitating a chest x-ray (CXR) as a routine part of screening. An AI-based model to predict COVID-19 likelihood from CXR findings can serve as an important and immediate adjunct to accelerate clinical decision making.Objective: To develop a robust AI-based diagnostic model to identify CXRs with COVID-19 compared with all non-COVID-19 CXRs. Setting: Labeled frontal CXR images (samples of COVID-19 and non-COVID-19) from the M Health Fairview (Minnesota, USA), Va- Valencian Region Medical ImageBank (Spain), MIMIC-CXR, Open-i 2013 Chest X-ray Collection, GitHub COVID-19 Image Data Collection (International). Main Outcome and Measure: Model performance assessed via Area under the Receiver Operating Curve (AUROC) and Area Under the Precision and Recall Curve (AUPRC). Results: Patients with COVID-19 had significantly higher COVID- 19 Diagnostic Scores than patients without COVID-19 on both real-time electronic health records and external (non-publicly available) validation. The model performed well across all four methods for model validation with AUROCs ranging between 0.7 – 0.96 and high PPV and specificity. The model performed had improved discrimination for patients with “severe” as compared to “moderate” COVID-19 disease. The model had unrealistic performance using publicly available databases, reflecting the inherent limitations in many previously developed models relying on publicly available data for training and validation. Conclusions and Relevance: AI-based diagnostic tools may serve as an adjunct, but not replacement, to support COVID-19 diagnosis which largely hinges on exposure history, signs, and symptoms. Future research should focus on optimizing discrimination of “mild” COVID-19 from non-COVID-19 image findings.