The linchpin is the system’s ability to analyze long-underused heart imaging, alongside a full spectrum of medical records, to reveal previously hidden information about a patient’s heart health. The federally funded work, led by Johns Hopkins University researchers, could save many lives and also spare many people unnecessary medical interventions, including the implantation of unneeded defibrillators.

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, a researcher focused on using artificial intelligence in cardiology. We have the ability to predict with very high accuracy whether a patient is at very high risk for sudden cardiac death or not. 

Hypertrophic cardiomyopathy is one of the most common inherited heart diseases, affecting one in every 200 to 500 individuals worldwide, and is a leading cause of sudden cardiac death in young people and athletes. Many patients with hypertrophic cardiomyopathy will live normal lives, but a percentage are at significant increased risk for sudden cardiac death. It’s been nearly impossible for doctors to determine who those patients are.

Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS), predicts individual patients’ risk for sudden cardiac death by analyzing a variety of medical data and records, and, for the first time, exploring all the information contained in the contrast-enhanced MRI images of the patient’s heart. People with hypertrophic cardiomyopathy develop fibrosis, or scarring, across their heart and it’s the scarring that elevates their risk of sudden cardiac death. While doctors haven’t been able to make sense of the raw MRI images, the AI model zeroed right in on the critical scarring patterns.

Source: https://www.bme.jhu.edu/news-events/news/ai-predicts-patients-likely-to-die-of-sudden-cardiac-arrest/