AI model predicts sudden cardiac death more accurately
MAARS, an AI tool for heart risk prediction, offers improved accuracy in detecting arrhythmia-related deaths in hypertrophic cardiomyopathy cases.
A new AI tool developed by researchers at Johns Hopkins University has shown promise in predicting sudden cardiac death among people with hypertrophic cardiomyopathy (HCM), outperforming existing clinical tools.
The model, known as MAARS (Multimodal AI for ventricular Arrhythmia Risk Stratification), uses a combination of medical records, cardiac MRI scans, and imaging reports to assess individual patient risk more accurately.
In early trials, MAARS achieved an AUC (area under the curve) score of 0.89 internally and 0.81 in external validation — both significantly higher than traditional risk calculators recommended by American and European guidelines.
The improvement is attributed to its ability to interpret raw cardiac MRI data, particularly scans enhanced with gadolinium, which are often overlooked in standard assessments.
While the tool has the potential to personalise care and reduce unnecessary defibrillator implants, researchers caution that the study was limited to small cohorts from Johns Hopkins and North Carolina’s Sanger Heart & Vascular Institute.
They also acknowledged that MAARS’s reliance on large and complex datasets may pose challenges for widespread clinical use.
Nevertheless, the research team believes MAARS could mark a shift in managing HCM, the most common inherited heart condition.
By identifying hidden patterns in imaging and medical histories, the AI model may protect patients more effectively, especially younger individuals who remain at risk yet receive no benefit from current interventions.
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