Artificial Intelligence Analysis of ECG to Determine Fractional Flow Reserve (FFR)

Abstract

Background: The current gold standard of coronary artery disease (CAD) diagnosis is invasive angiography, during which fractional flow reserve (FFR) measurement may be performed to confirm the clinical significance of a stenosis. The yield of routine and indiscriminate FFR in identifying hemodynamically significant stenoses is low. To combat this, we have developed an artificial intelligence model, ECGio, designed to be deployed at the point of care to determine FFR through the analysis of a resting digital 12-lead electrocardiogram (ECG), a fast, real-time, cost-effective, widely accessible, and safe diagnostic method. This study assessed the ability of ECGio to train, tune, and test itself through a cross-validation paradigm to predict the presence of a reduced FFR in the left anterior descending artery in a patient population presenting for invasive FFR. Methods: In a single-center study the ECGs of 209 consecutive patients (61.3 ± 9.5 years, 35.4% female) from 2014 to 2021 were recorded within 7 days prior to angiography during which FFR was measured in the left anterior descending artery. Collected ECGs were used to train and test the AI model using a five-fold cross-validation methodology. Results: The ability of ECGio to predict the presence of a reduced FFR (<0.80) in this cohort was a sensitivity, specificity, PPV, NPV, Accuracy, and F-1 Score of 43.2%, 86.7%, 64.0%, 73.6%, 71.3%, and 51.6%, respectively. Conclusions: This study demonstrated the feasibility of using a deep learning AI algorithm to analyze a digital 12-lead ECG to provide a similar level of information as the invasive FFR.