Researchers at the Mount Sinai Kravis Children's Heart Center have led a multicenter study to develop and validate an artificial intelligence (AI) tool that analyzes standard electrocardiograms (ECGs) to identify patients with repaired tetralogy of Fallot who may be at risk for harmful heart changes. These changes, known as ventricular remodeling, are typically detected by cardiac MRI. The study was supported by the National Institutes of Health and published in the European Heart Journal: Digital Health.
Tetralogy of Fallot is a congenital heart defect that requires surgical repair during childhood. Patients who undergo this surgery need lifelong monitoring to detect alterations in heart size and function. Cardiac MRI is considered the gold-standard test for such follow-up care, but it can be expensive, time-consuming, and not always accessible. As a result, many patients do not receive recommended imaging.
In this study, researchers trained an AI model using both ECG and MRI data from patients with repaired tetralogy of Fallot. The model was then validated across five additional hospitals in North America. The AI system learned to recognize patterns in ECG signals associated with ventricular remodeling—changes in heart size and pumping ability that may indicate worsening health.
The research team clarified that their AI model is not meant to replace cardiac MRI but could help clinicians determine when advanced imaging is most urgently needed.
"As AI becomes more integrated into health care, it is critical to rigorously validate these tools across diverse clinical settings," said Girish Nadkarni, MD, MPH, co-senior author and Barbara T. Murphy Chair of the Windreich Department of Artificial Intelligence and Human Health at Mount Sinai Health System. "Our findings show both the promise of AI-enabled screening and the importance of testing performance at each site before real-world implementation." Dr. Nadkarni also serves as Director of the Hasso Plattner Institute for Digital Health and Chief AI Officer at Icahn School of Medicine at Mount Sinai.
Patients with congenital heart disease often require ongoing specialized care throughout their lives. By combining AI technology with simple ECG tests, researchers aim to improve detection methods for those needing further evaluation.
The team plans to continue testing the AI-ECG approach through prospective clinical studies and trials while refining the model for use in younger patients. Their long-term goal is to integrate this tool into routine clinical practice.
