Using AI to predict heart function in patients with repaired tetralogy of Fallot

Precision Prediction of Right Ventricular Size, Function, and Outcomes in Patients with Repaired Tetralogy of Fallot

NIH-funded research Icahn School of Medicine at Mount Sinai · NIH-11055102

This study is looking to make it easier for people who have had surgery for tetralogy of Fallot to understand their heart health by using advanced technology to analyze heart tests, so they can avoid extra MRIs and get better care.

Quick facts

Grant typeCareer grant
Study typeNIH-funded research
Funding institutionIcahn School of Medicine at Mount Sinai NIH-funded
Lab location1 site (New York, United States)
Project IDNIH-11055102 on NIH RePORTER

What this research studies

This research focuses on improving the prediction of right ventricular size and function in patients who have undergone repair for tetralogy of Fallot, a congenital heart defect. By utilizing advanced artificial intelligence techniques, the study aims to analyze data from electrocardiograms and echocardiograms to provide more accurate risk assessments for heart failure and other complications. This approach seeks to reduce the need for frequent cardiac MRIs, which can be burdensome for patients. The ultimate goal is to enhance patient management and outcomes through better risk stratification.

Who could benefit from this research

Good fit: Ideal candidates for this research are individuals aged 12 years and older who have undergone surgical repair for tetralogy of Fallot.

Not a fit: Patients without a history of tetralogy of Fallot or those who are not within the specified age range may not benefit from this research.

Why it matters

Potential benefit: If successful, this research could lead to more accurate monitoring and management of heart health in patients with repaired tetralogy of Fallot, potentially reducing complications and improving quality of life.

How similar studies have performed: Previous research has shown promise in using AI for cardiovascular imaging and risk prediction, indicating that this approach could be effective.

Where this research is happening

New York, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-13 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.