Mount Sinai Develops AI Algorithm Tool to Detect Hypertrophic Cardiomyopathy

The AI tool has received approval from the U.S. Food and Drug Administration (FDA) for detection using electrocardiogram (ECG) data.

Researchers from Mount Sinai, New York City, have developed an artificial intelligence (AI) algorithm designed to help clinicians identify patients at risk of hypertrophic cardiomyopathy (HCM). 

The tool, which assigns a numeric probability to assess the risk of HCM, has received approval from the U.S. Food and Drug Administration (FDA) for detection using electrocardiogram (ECG) data.

According to an announcement on April 22, the AI model aims to improve clinical workflows by prioritizing high-risk patients. 

"This is an important step forward in translating novel deep-learning algorithms into clinical practice," said Dr. Joshua Lampert, Director of Machine Learning at Mount Sinai Fuster Heart Hospital. 

"Clinicians can improve their workflows by ensuring the highest-risk patients are identified at the top of their clinical work list using a sorting tool."

Researchers plan to expand the study of the algorithm in collaboration with health systems nationwide.

AI Center for Children's Health Launched

In a separate development, the Icahn School of Medicine at Mount Sinai recently launched the Center for Artificial Intelligence (AI) in Children's Health—the first dedicated initiative of its kind in New York City. 

The center was established under The Mindich Child Health and Development Institute and is co-sponsored by Mount Sinai's Windreich Department of Artificial Intelligence and Human Health.

Dr. Benjamin S. Glicksberg, an expert in digital health and clinical informatics, has been appointed to lead the center. 

With a background in genomics and AI-driven healthcare innovation, Dr. Glicksberg will guide the center's efforts to use AI to enhance diagnostics, personalize treatments, and improve care delivery for pediatric patients.

The initiative also aims to address the regulatory complexities unique to pediatric healthcare.

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