Abstract 11282: Artificial Intelligence Derived Algorithm to Screen for Ebstein’s Anomaly of the Tricuspid Valve Utilizing Echocardiography

Introduction: Ebstein’s anomaly (EA) is a rare congenital anomaly of the tricuspid valve associated with heart failure and arrhythmias. Currently available imaging modalities like transthoracic echocardiography, cardiac magnetic resonance imaging (MRI), or computerized tomography (CT) are not viable for screening large asymptomatic populations. Hypothesis: A deep learning algorithm, implemented using a convolutional neural network (CNN) applied to the standard 12-lead ECG can be used as an accurate screening tool for EA in children and adults. Method: We identified 1,154 EA patients and 21,430 age- and sex-matched controls (1:20 ratio) between 1987 - 2020. A CNN was trained, validated and tested using digital 12-lead ECGs in 70%, 10% and 20% of the cohort respectively, divided by random shuffling. Results: The mean age was 26.68±19.23 years (41.8% aged < 18 years, 56.41% female) for the EA cohort and 28.36±18.65 years (37.2% aged <18 years, 55.67% female) for the control cohort. Following training and validation the area under the curve (AUC) for detecting EA in the testing cohort was 0.976 (95% CI 0.962 - 0.989) with prediction accuracy of 0.959. In subgroup analysis, the AUC was comparable in males (0.970, 95% CI 0.946-0.995) and females (0.979, 95% CI 0.964-0.994), those <18 (0.980, 95% CI 0.958-1.000) and ≥18 years old (0.972, 95% CI 0.954-0.989), as well as in those with right bundle branch block (RBBB) (0.969, 95% CI 0.950-0.989) and without RBBB (0.950, 95% CI 0.922-0.978) on ECG. Conclusion: Artificial intelligence enhanced ECG based detection of EA is possible with high diagnostic accuracy. Following further refinement and external validation, this algorithm can facilitate early detection of EA using ECGs, a widely available and inexpensive tool. Figure: Receiver operating characteristic (ROC) curve for the performance of the artificial intelligence CNN in detecting EA in the test cohort stratified by (A) age (< 18 and ≥ 18 years) and sex and (B) presence of RBBB.