DaijiWorld Media Network -New York
New York, July 5: In a breakthrough in cardiovascular medicine, US researchers have developed an advanced artificial intelligence (AI) system (SCD) that dramatically improves the accuracy of identifying patients at high risk of sudden cardiac death. A new model, dubbed multimodal AI for ventricular arrhythmias risk stratification (MAAR), surpasses existing clinical guidelines and allows physicians to reconstruct the way they assess cardiac risk.
Developed at Johns Hopkins University, MAARS integrates cardiac MRI scans with a wide range of patient health data to reveal subtle warning signs that traditional methods have missed, according to a report by Xinhua News Agency. The findings were published in the journal Nature Cardiovascular Research.
This study focused in particular on hypertrophic cardiomyopathy (HCM). This is a common genetic heart condition and is one of the leading causes of sudden cardiac death in young people.
“Now, existing tools can’t pinpoint who truly need protection, so we’re losing patients at the height of our lives. At the same time, we live with defibrillators that others may never actually need,” says Natalia Trejanova, an AI-based cardiac specialist at Johns Hopkins.
The average predictive accuracy for current US and European clinical guidelines is approximately 50%, while the MAARS model achieved an overall accuracy of 89%. For patients aged 40-60 (the most at-risk age group), the model was even more accurate, reaching 93%.
The secret to Maars’ success lies in the use of contrast-enhancing MRI scans that highlight the scar tissue of the heart. This is an important predictor of arrhythmia that leads to sudden cardiac death. While these scans are traditionally challenged by physicians to interpret, Maars uses deep learning to analyze subtle patterns and derive risk factors that the human eye may miss.
“We’ve seen a lot of fun and hard work,” said Dr. Jonathan Crispin, co-author and cardiologist at Johns Hopkins. “Compared to current risk calculators, MAARS significantly improves our ability to most identify those who need interventions.”
Looking ahead, the researchers plan to expand the model’s applications to other serious cardiac conditions, such as cardiac sarcoidosis and arrhythmic right ventricular cardiomyopathy, potentially becoming a game-changer for personalized cardiac care.
