Mayourian, Joshua

Purpose of review Artificial intelligence applied to electrocardiography (AI-ECG) has rapidly been investigated in adult cardiovascular medicine, yet translation into pediatric and congenital heart disease populations has lagged. This review summarizes contemporary AI-ECG methodologies and emerging applications in pediatric and congenital heart disease (PCHD), with emphasis on current clinical utility, technical challenges, and future opportunities for implementation. Recent findings Recent studies demonstrate that deep learning models can accurately identify arrhythmias, ventricular dysfunction, and CHD from standard ECG. Convolutional neural networks remain the dominant architecture, although transformer-based foundation models and self-supervised learning approaches are increasingly being explored. AI-ECG applications in PCHD have expanded from automated interpretation toward proactive risk stratification, including prediction of ventricular dysfunction, mortality, and sudden cardiac death risk. Additional work has investigated wearable monitoring, telemetry analysis, and integration with longitudinal clinical data. Despite promising performance, most studies remain retrospective and single-center, with limited external validation and challenges related to small datasets, physiologic heterogeneity, and age-dependent ECG variation. Summary AI-ECG has the potential to transform PCHD by improving diagnostic accuracy, enabling earlier disease detection, and enhancing longitudinal risk assessment. Broader clinical implementation will require multicenter collaboration, prospective validation, standardized datasets, and careful attention to ethical, regulatory, and equity considerations.

Abstract Aims To assess the potential for artificial intelligence-enabled electrocardiogram (AI-ECG) to serve as a long-term cardiac surveillance tool and predict left ventricular systolic dysfunction in childhood cancer patients. Methods and results We assessed performance of our previously established AI-ECG model to predict left ventricular ejection fraction (LVEF) ≤50% and ≤40% in patients with childhood cancer during internal testing (Boston Children’s Hospital) and external validation (Children’s Hospital of Philadelphia). The internal test cohort comprised 447 patients [57% male; age at cancer diagnosis 11.2 (5.4–15.7) years; 1553 ECG–echo pairs at median age 13.5 (IQR 7.7–17.9) years; 6.4% with LVEF ≤50%; 1.3% with LVEF ≤40%], 28% with leukaemia, 16% with lymphoma, 8% with neuroblastoma, 8% with sarcoma, 2% with gastrointestinal cancers, 3% with genitourinary cancers, 6% with central nervous system cancers, 11% with other/unspecified cancers, and 18% with missing/unknown cancer labels. Treatment strategies included anthracyclines (35%), bone marrow transplant (7%), and radiation (1%). The external test cohort comprised 2964 patients [55.4% male; 7054 ECG–echo pairs at median age 11.6 (IQR 6.8–15.1) years; 2.5% with LVEF ≤50%; 0.9% with LVEF ≤40%]. Similar AUROCs (0.80–0.85), sensitivities (0.75–0.82), NPVs (0.986–0.996), and percent predicted negative (51–65%) were obtained across institutions to predict LVEF ≤50%, outperforming a biomarker-based model benchmark. Patients with high AI-ECG risk scores for LVEF ≤50% had higher rates of mortality [hazard ratio 3.1 (95% CI 1.8–5.3), P < 0.001] compared to patients with low AI-ECG risk scores. Conclusion AI-ECG shows promise as a digital biomarker for cardiac surveillance in the vulnerable childhood cancer survivor population.