Mount Sinai researchers studying a type of heart disease known as hypertrophic cardiomyopathy (HCM) 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 for the detection of HCM on an electrocardiogram (ECG). The Mount Sinai study, published April 22 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," says corresponding author Joshua Lampert, MD, 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. Patients can be better counseled by receiving more individualized information through model calibration which improves interpretability of model classification scores. Whether this local model calibration strategy is universally applicable to other settings remains to be demonstrated," says Dr. Lampert, Assistant Professor of Medicine (Cardiology, and Data-Driven and Digital Medicine) at the Icahn School of Medicine at Mount Sinai. "This can transform clinical practice because the approach provides meaningful information in a clinically pragmatic fashion to facilitate patient care."
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.
The Mount Sinai researchers ran the Viz HCM algorithm on nearly 71,000 patients who had an electrocardiogram between March 7, 2023, and January 18, 2024. The algorithm flagged 1,522 as having a positive alert for HCM. Researchers reviewed the records and imaging data to confirm which patients had a confirmed HCM diagnosis.
After reviewing the confirmed diagnoses, researchers applied model calibration to the AI tool to assess whether the calibrated probability of having HCM correlated with the actual likelihood of patients having the disease. They found that—the calibrated model did give an accurate estimate of a patient's likelihood of having HCM.
Using the model to analyze patients' ECG results could allow cardiologists to prioritize the highest-risk patients to bring them in sooner for an appointment and treatment before symptoms begin or exacerbate. Doctors will be able to explain the individualized risk to each patient, rather than stating vaguely that an AI model flagged them. This may help get new patients engaged and into care to prevent adverse outcomes associated with HCM, such as sudden death or symptoms from the thickened heart muscle obstructing blood flow.
"This study provides much-needed granularity to help rethink how we triage, risk-stratify, and counsel patients. In an era of augmented intelligence, we must grow to incorporate novel sophistication in our approach to patient care," says co-senior author Vivek Reddy, MD , Director of Cardiac Arrhythmia Services for the Mount Sinai Health System and the Leona M. and Harry B. Helmsley Charitable Trust Professor of Medicine in Cardiac Electrophysiology. "Using hypertrophic cardiomyopathy as an illustrative use case, we show how we can pragmatically operationalize novel tools even in the setting of less common diseases by sorting AI classifications to triage patients."
"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," says co-senior author Girish N. Nadkarni, MD, MPH , Chair of the Windreich Department of Artificial Intelligence and Human Health , Director of the Hasso Plattner Institute for Digital Health , and Irene and Dr. Arthur M. Fishberg Professor of Medicine at the Icahn School of Medicine at Mount Sinai. "It's not just about building a high-performing algorithm—it's about making sure it supports clinical decision-making in a way that improves patient outcomes and aligns with how care is actually delivered. This work shows how a calibrated model can help clinicians prioritize the right patients at the right time, and in doing so, help realize the full potential of AI in medicine."
The next step is to expand this study and AI calibration for HCM to additional health systems across the country.
Viz.ai sponsored this study. Dr. Lampert is a paid consultant for Viz.ai.
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