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AI Spots Deadly Heart Risk Most Doctors Can't See
JOHNS HOPKINS UNIVERSITY, BALTIMORE, MARYLAND, USA, JUL 2 – The MAARS AI model improves prediction of sudden cardiac death risk in hypertrophic cardiomyopathy patients to 89% accuracy, nearly doubling current clinical guideline performance.
- On July 2, Johns Hopkins University researchers unveiled Multimodal AI for ventricular Arrhythmia Risk Stratification, which predicts sudden cardiac arrest risk by analyzing medical records and contrast-enhanced MRI images.
- Current clinical guidelines identify patients only about half the time, said Natalia Trayanova, senior author and AI cardiology researcher, hindering risk assessment.
- In testing, MAARS achieved 89% accuracy across all patients, with accuracy rising to 93% for patients aged 40 to 60.
- The study could save many lives, and MAARS explains high-risk factors to help tailor individual treatment plans.
- In future studies, the research team plans to test MAARS on more patients and expand to other heart diseases, which could enhance personalized risk assessment and clinical care.
26 Articles
26 Articles
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Science Daily
Verve timesAI spots deadly heart risk most doctors can't see
An advanced Johns Hopkins AI model called MAARS combs through underused heart MRI scans and complete medical records to spot hidden scar patterns that signal sudden cardiac death, dramatically outperforming current dice-roll clinical guidelines and promising to save lives while sparing patients unnecessary defibrillators.
MedPage Today
Anti-Aging, Acupuncture and Health NewsAI Model Better Predicts Sudden Deaths in Hypertrophic Cardiomyopathy
(MedPage Today) -- Artificial intelligence (AI) showed promise for substantially improving risk prediction in hypertrophic cardiomyopathy (HCM) beyond what clinicians can do with current tools, researchers reported. A deep learning model, Multimodal...
Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy
Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhythmic death prognostication is challenging in patients with hypertrophic cardiomyopathy (HCM), a setting where current clinical guidelines show low performance and inconsistent accuracy. Here, we present a deep learning approach, MAARS (Multimodal Artificial intelligence for ventricular Arrhythmia Risk Stratification), to forecast lethal arrhythmia …
AI predicts patients likely to die of sudden cardiac arrest
A new AI model is much better than doctors at identifying patients likely to experience cardiac arrest. 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.
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