LOS ANGELES, July 4 (Xinhua) -- Researchers at Johns Hopkins University have developed a new artificial intelligence (AI) model that significantly outperforms current clinical guidelines in identifying patients at high risk of sudden cardiac death, according to a newly published study.
The AI system, known as Multimodal AI for ventricular Arrhythmia Risk Stratification (MAARS), integrates cardiac MRI images with a wide range of patient health records to detect hidden warning signs, offering a new level of precision in cardiovascular risk prediction.
The study, published this week in Nature Cardiovascular Research, focused on hypertrophic cardiomyopathy, one of the most common inherited heart conditions and a leading cause of sudden cardiac death in young people.
"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 AI 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."
Clinical guidelines used in the United States and Europe currently have an estimated accuracy of only 50 percent in identifying at-risk patients. In contrast, the MAARS model demonstrated an overall accuracy of 89 percent, and 93 percent for patients aged 40 to 60 -- the group at the greatest risk.
The AI model analyzes contrast-enhanced MRI scans for patterns of heart scarring -- something that physicians have traditionally found difficult to interpret. By applying deep learning to this previously underused data, the model identifies key predictors of sudden cardiac death.
"Our study demonstrates that the AI model significantly enhances our ability to predict those at highest risk compared to our current algorithms and thus has the power to transform clinical care," said co-author Jonathan Chrispin, a Johns Hopkins cardiologist.
The team plans to further test the new model on more patients and expand the new algorithm to use with other types of heart diseases, including cardiac sarcoidosis and arrhythmogenic right ventricular cardiomyopathy.
