Enhancement of Stress ECG Performance with Machine Learning: A Single-Center Study

Exercise stress electrocardiogram (ECG) (ESE) is a widely used, noninvasive diagnostic tool for detecting coronary artery disease (CAD). Despite its widespread use, the diagnostic accuracy of ESE remains suboptimal.

This study aimed to develop and evaluate an artificial intelligence (AI) model, using a transformer-based architecture, to enhance the diagnostic performance ofESEs.

Patients who underwent coronary angiography within 2 months of the ESE were eligible for inclusion. An AI model processed exercise stress ECG images into time-series data. A transformer-based architecture was employed to integrate temporal ECG features and predict CAD. Model performance in predicting severe CAD was first evaluated using 5-fold cross-validation on a test subset from the original cohort, and subsequently on a second validation cohort.

We developed a model using a total of 1,200 ECGs. An additional validation cohort of 91 patients was also analyzed. On the initial test subset, the AI model demonstrated a sensitivity of 93.6%, specificity of 93.2%, and overall accuracy of 93.4%. Notably, the model improved sensitivity with an absolute increase of 40.9% in women and 44.6% in men. In the second validation cohort, the model achieved an accuracy of 78%, with a sensitivity of 64.6% and a specificity of 93%.

This study presents a proof of concept demonstrating that an AI-based model for stress ECG interpretation is feasible and shows acceptable performance.