AI-Driven ECG to Aid Lifelong Heart Monitoring

Mount Sinai researchers develop AI tool to identify heart changes in patients with repaired congenital defects using standard ECG data

Published on Feb. 23, 2026

Researchers at the Mount Sinai Kravis Children's Heart Center led a multicenter effort to develop and validate an artificial intelligence (AI) tool that can analyze a standard electrocardiogram (ECG) to identify patients with repaired tetralogy of Fallot who may be at risk for harmful heart changes typically detected by cardiac MRI. The AI-powered ECG screening could help improve access to advanced imaging and personalize follow-up care for patients with congenital heart disease.

Why it matters

Patients with congenital heart disease often require lifelong specialized follow-up care, but cardiac MRI scans - the gold standard for monitoring - are expensive, time-consuming, and not always accessible. This AI-driven ECG tool could help reduce unnecessary testing, improve access to advanced imaging for high-risk patients, and personalize long-term care to improve outcomes.

The details

The researchers trained an AI model using ECG and MRI data from patients with repaired tetralogy of Fallot, a common congenital heart defect. The AI learned to detect patterns in ECG signals linked to ventricular remodeling - changes in heart size and pumping function that can signal worsening health. The model was then validated across five additional hospitals in North America. The key findings show the AI-powered ECG can estimate a patient's risk of ventricular remodeling, potentially helping doctors prioritize MRI scans for higher-risk patients while safely delaying scans for lower-risk patients.

  • The study was published in the European Heart Journal: Digital Health in February 2026.

The players

Mount Sinai Kravis Children's Heart Center

A leading pediatric heart center that led the multicenter research effort to develop the AI-driven ECG tool.

Son Duong, MD, MS

The lead author of the study and an Assistant Professor of Pediatrics, and Artificial Intelligence and Human Health at Icahn School of Medicine at Mount Sinai.

Girish Nadkarni, MD, MPH

The co-senior author of the study, the Barbara T. Murphy Chair of the Windreich Department of Artificial Intelligence and Human Health at Mount Sinai Health System, and the Director of the Hasso Plattner Institute for Digital Health and Chief AI Officer at Icahn School of Medicine at Mount Sinai.

Got photos? Submit your photos here. ›

What they’re saying

“This research shows how artificial intelligence can unlock new value from a routine ECG. Our goal is to make lifelong heart monitoring more accessible and efficient for people born with congenital heart disease.”

— Son Duong, Assistant Professor of Pediatrics, and Artificial Intelligence and Human Health at Icahn School of Medicine at Mount Sinai (Mirage News)

“As AI becomes more integrated into health care, it is critical to rigorously validate these tools across diverse clinical settings. Our findings show both the promise of AI-enabled screening and the importance of testing performance at each site before real-world implementation.”

— Girish Nadkarni, Barbara T. Murphy Chair of the Windreich Department of Artificial Intelligence and Human Health, Mount Sinai Health System; Director of the Hasso Plattner Institute for Digital Health and Chief AI Officer, Icahn School of Medicine at Mount Sinai (Mirage News)

What’s next

The research team plans to test the AI-ECG approach in prospective clinical studies and trials and refine the model for younger patients. The long-term goal is to integrate the tool into routine clinical care.

The takeaway

This AI-powered ECG screening tool has the potential to improve access to advanced cardiac imaging, personalize follow-up care, and reduce unnecessary testing for patients with congenital heart disease - helping to ensure these patients receive the lifelong monitoring they need.