A new AI algorithm leveraging cardiac MRI and health data significantly outperforms current clinical guidelines in predicting risk of sudden cardiac arrest in hypertrophic cardiomyopathy patients.
Key Details
- 1The AI model, MAARS, uses cardiac MRI, electronic health records, and echocardiogram data.
- 2Developed and validated on cohorts of 553 and 284 patients, respectively.
- 3MAARS achieved 89% overall accuracy and 93% accuracy for high-risk patients aged 40-60.
- 4Outperformed traditional clinical guidelines (approx. 50% accuracy) and other tools in sensitivity, specificity, and AUROC (0.89 for MAARS).
- 5Could reduce unnecessary implantable defibrillators and more precisely identify at-risk patients.
Why It Matters

Source
AuntMinnie
Related News

Literature Review Highlights Gaps in Economic Evaluation of Healthcare AI
A Finnish review finds significant gaps in economic evaluation reporting of AI technologies in Western healthcare.

Economic Evaluations of AI in Healthcare Face Major Gaps
A Finnish review finds significant deficiencies in how studies evaluate and report the economic impact of healthcare AI.

AI Platform Triples Functional Independence in UK Stroke Patients
AI brain imaging software deployed by NHS has significantly improved stroke outcomes and functional independence rates in England.