Deep Learning-Based Electrocardiogram Analysis Predicts Biventricular Dysfunction and Dilation in Congenital Heart Disease

Background: Artificial intelligence–enhanced ECG (AI-ECG) analysis shows promise to detect biventricular pathophysiology. However, AI-ECG analysis remains underexplored in congenital heart disease (CHD). Objectives: To develop and externally validate an AI-ECG model to predict cardiovascular magnetic resonance (CMR)-defined biventricular dysfunction/dilation in patients with CHD. Methods: We trained (80%) and tested (20%) a convolutional neural network on paired ECG-CMRs (≤30 days apart) from patients with and without CHD to detect left ventricular (LV) dysfunction (ejection fraction ≤40%), RV dysfunction (ejection fraction ≤35%), and LV and RV dilation (end-diastolic volume z-score ≥4). Performance was assessed during internal testing and external validation on an outside healthcare system using area under receiver operating (AUROC) and precision recall (AUPRC) curves. Results: The internal and external cohorts comprised of 8,584 ECG-CMR pairs (4,941 patients; median CMR age 20.7 years) and 909 ECG-CMR pairs (746 patients; median CMR age 25.4 years), respectively. Model performance was similar for internal testing (AUROC: LV dysfunction 0.87; LV dilation 0.86; RV dysfunction 0.88; RV dilation 0.81) and external validation (AUROC: LV dysfunction 0.89; LV dilation 0.83; RV dysfunction 0.82; RV dilation 0.80). Model performance was lowest in functionally single ventricle patients. Tetralogy of Fallot patients predicted at high-risk of ventricular dysfunction had lower survival (p<0.001). Model explainability via saliency mapping revealed lateral precordial leads influence all outcome predictions, with high-risk features including QRS widening and T wave inversions for RV dysfunction/dilation. Conclusions: AI-ECG shows promise to predict biventricular dysfunction/dilation, which may help inform CMR timing in CHD.