Deep learning to detect significant coronary artery disease from plain chest radiographs

Abstract

Abstract Background Coronary artery disease (CAD) is the single leading cause of mortality, premature death, and morbidity worldwide. Artificial intelligence (AI) could help identify markers present within first-line diagnostic imaging routinely performed in patients referred for suspected angina, such as chest radiographs. Purpose To train, test, and validate a deep learning (DL) algorithm for detecting the presence of significant CAD based on chest radiographs. Methods Data of patients undergoing chest radiography and coronary angiography were retrospectively analysed. A deep convolutional neural network (DCNN) was designed to detect significant CAD from the patient posteroanterior/anteroposterior chest radiograph. The DCNN was trained for binary classification of severe CAD absence/presence (at least one diseased coronary vessel with ≥70% stenosis). Coronary angiography reports were used as the ground truth. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the DCNN were calculated. Multivariate analysis was performed to identify independent correlation among the presence of significant CAD (dependent variable), DCNN prediction, and CAD risk factors. Results Information of 7728 patients referred for suspected angina was reviewed. Severe CAD was present in 4482 patients (58%; 1% left main, 28% one vessel, 16% two vessels, and 12% 3 vessels). Patients were randomly divided for training (70%; n=5454) and fine-tuning/testing (10%; n=773) of the algorithm. Internal validation was performed with the remaining patients (20%; n=1501). At binary logistic regression, the DCNN prediction was the strongest independent determinant of severe CAD (p<0.0001; OR: 52.8; CI: 25.1–110.9). Age (p=0.008; OR: 1.01; CI: 1.0–1.02) and Diamond-Forrester score (p<0.0001; OR: 1.022; CI: 1.018–1.026) were also independently related to CAD, although with lower significance and odds-ratios. Using an operating cut-point with high sensitivity, the DCNN had a sensitivity of 0.90 and specificity of 0.31 to detect significant CAD in the internal validation group (AUC 0.73; 95% CI DeLong, 0.69–0.76). Adding to the AI chest radiograph interpretation, patient age and angina status improved the prediction (AUC 0.77; 95% CI DeLong, 0.74–0.80). Conclusion The chest radiograph is ubiquitous and carries a plethora of information concerning the patient's health status, including direct and indirect signs of CAD. Our DL algorithm can predict, with high sensitivity, the presence of severe CAD in patients referred for suspected angina. It could be used to pre-test significant CAD probability in outpatient clinics, emergency room settings, and CAD screening in more extensive settings. Further studies are required to externally validate the algorithm and develop a clinically applicable tool. Funding Acknowledgement Type of funding sources: None.