AI algorithm can help identify high-risk heart patients: Study
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New York, April 22: A team of US researchers, studying a type of heart disease known as hypertrophic cardiomyopathy (HCM), on Tuesday said they have calibrated an artificial intelligence (AI) algorithm to quickly and more specifically identify patients with the condition and flag them as high risk for greater attention during doctor’s appointments.
The algorithm, known as Viz HCM, had previously been approved by the Food and Drug Administration (FDA) for the detection of HCM on an electrocardiogram (ECG).
The Mount Sinai study, published in the journal NEJM AI, assigns numeric probabilities to the algorithm’s findings.
For example, while the algorithm might previously have said "flagged as suspected HCM" or "high risk of HCM," the Mount Sinai study allows for interpretations such as, "You have about a 60 percent chance of having HCM," said Joshua Lampert, Director of Machine Learning at Mount Sinai Fuster Heart Hospital.
As a result, patients who had not previously been diagnosed with HCM may be able to get a better understanding of their individual disease risk, leading to a faster and more individualized evaluation, along with treatment to potentially prevent complications such as sudden cardiac death, especially in young patients.
“This is an important step forward in translating novel deep-learning algorithms into clinical practice by providing clinicians and patients with more meaningful information. Clinicians can improve their clinical workflows by ensuring the highest-risk patients are identified at the top of their clinical work list using a sorting tool,” said Lampert, Assistant Professor of Medicine (Cardiology, and Data-Driven and Digital Medicine) at the Icahn School of Medicine at Mount Sinai.
HCM impacts one in 200 people worldwide and is a leading reason for heart transplantation. However, many patients don’t know they have the condition until they have symptoms and the disease may already be advanced.
“This study reflects pragmatic implementation science at its best, demonstrating how we can responsibly and thoughtfully integrate advanced AI tools into real-world clinical workflows,” said co-senior author Girish N Nadkarni, Chair of the Windreich Department of Artificial Intelligence and Human Health and Director of the Hasso Plattner Institute for Digital Health.
The algorithm, known as Viz HCM, had previously been approved by the Food and Drug Administration (FDA) for the detection of HCM on an electrocardiogram (ECG).
The Mount Sinai study, published in the journal NEJM AI, assigns numeric probabilities to the algorithm’s findings.
For example, while the algorithm might previously have said "flagged as suspected HCM" or "high risk of HCM," the Mount Sinai study allows for interpretations such as, "You have about a 60 percent chance of having HCM," said Joshua Lampert, Director of Machine Learning at Mount Sinai Fuster Heart Hospital.
As a result, patients who had not previously been diagnosed with HCM may be able to get a better understanding of their individual disease risk, leading to a faster and more individualized evaluation, along with treatment to potentially prevent complications such as sudden cardiac death, especially in young patients.
“This is an important step forward in translating novel deep-learning algorithms into clinical practice by providing clinicians and patients with more meaningful information. Clinicians can improve their clinical workflows by ensuring the highest-risk patients are identified at the top of their clinical work list using a sorting tool,” said Lampert, Assistant Professor of Medicine (Cardiology, and Data-Driven and Digital Medicine) at the Icahn School of Medicine at Mount Sinai.
HCM impacts one in 200 people worldwide and is a leading reason for heart transplantation. However, many patients don’t know they have the condition until they have symptoms and the disease may already be advanced.
“This study reflects pragmatic implementation science at its best, demonstrating how we can responsibly and thoughtfully integrate advanced AI tools into real-world clinical workflows,” said co-senior author Girish N Nadkarni, Chair of the Windreich Department of Artificial Intelligence and Human Health and Director of the Hasso Plattner Institute for Digital Health.
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