The ECG-based deep learning model demonstrated an AUC of 0.693 for screening obstructive coronary artery disease, indicating modest performance compared to the excellent AUC of 0.923 for detecting acute myocardial infarction.
Observational (n=10,160)
No
Does an ECG-based deep learning algorithm accurately screen for obstructive coronary artery disease in patients undergoing coronary angiography for suspected CAD?
An ECG-based deep learning model demonstrated fair diagnostic performance (AUC 0.693) for screening obstructive coronary artery disease, suggesting potential utility as an adjunct tool during initial clinical evaluation.
Effect estimate: AUC 0.693
Abstract Background Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. Methods ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. Results The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. Conclusions ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.
Choi et al. (Wed,) conducted a observational in Obstructive coronary artery disease (ObCAD) (n=10,160). ECG-based deep learning algorithm vs. Standard ECG interpretation was evaluated on Screening for obstructive coronary artery disease (ObCAD) using ECG-based DL model (AUC 0.693). The ECG-based deep learning model demonstrated an AUC of 0.693 for screening obstructive coronary artery disease, indicating modest performance compared to the excellent AUC of 0.923 for detecting acute myocardial infarction.