AI Model Far Outperforms Doctors in Predicting Sudden Cardiac Death Risk: Study Finds

Hypertrophic cardiomyopathy, one of the most common inherited heart conditions, affects 1 in every 200 to 500 people worldwide and is a leading cause of sudden cardiac death, particularly among young individuals and athletes.

The AI model changes that by harnessing a wide range of patient data, including long-overlooked contrast-enhanced MRI images and full-spectrum electronic health records. These images, while difficult for human doctors to interpret in detail, contain critical patterns of fibrosis—or heart scarring—that are strongly associated with cardiac arrest risk.

Tested against real-world patient data from Johns Hopkins Hospital and the Sanger Heart & Vascular Institute in North Carolina, MAARS achieved 89% overall accuracy and 93% accuracy in patients aged 40 to 60—those most vulnerable to sudden cardiac death. Importantly, the AI model also explains why a patient is high risk, enabling doctors to tailor treatment more precisely.

“Currently we have patients dying in the prime of their life because they aren't protected and others who are putting up with defibrillators for the rest of their lives with no benefit,” said senior author Natalia Trayanova. “We have the ability to predict with very high accuracy whether a patient is at very high risk for sudden cardiac death or not.”

Reference: Changxin Lai, Minglang Yin, Eugene G. Kholmovski, Dan M. Popescu, Dai-Yin Lu, Erica Scherer, Edem Binka, Stefan L. Zimmerman, Jonathan Chrispin, Allison G. Hays, Dermot M. Phelan, M. Roselle Abraham, Natalia A. Trayanova. Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy. Nature Cardiovascular Research, 2025; DOI: 10.1038/s44161-025-00679-1