AI Can Accurately Predict Risk of Cardiovascular Disease, Detect Valvular Heart Disease

Two studies have shown that artificial intelligence (AI) and deep learning models have the potential to predict the risk of cardiovascular disease (CVD) events and detect valvular heart disease. The preliminary research studies will be presented during the American Heart Association’s Scientific Sessions 2023, November 11-13 in Philadelphia, Pennsylvania.

“Computational methods to develop novel predictors of health and disease—’artificial intelligence’—are becoming increasingly sophisticated,” said Dan Roden, MD, FAHA, a professor of medicine, pharmacology, and biomedical informatics and senior vice president for personalized medicine at Vanderbilt University Medical Center and chair of the Association’s Council on Genomic and Precision Medicine, via a press release. “Both of these studies take a measurement that is easy to understand and easy to acquire and ask what that measurement predicts in the wider world.”

In one study, conducted at 3 different US primary care clinics, researchers compared the ability of a medical professional using a standard stethoscope to detect potential valvular heart disease with the ability of an AI model, which uses sound data from a digital stethoscope to perform the same task. The study comprised 369 adults (aged 50 years and older; 61% female; 70% White) who were enrolled from June 2021 through May 2023.

The results showed that the AI-based digital stethoscope detected cases of valvular heart disease at a significantly higher rate than the standard stethoscope used by primary care professionals (94.1% vs 41.2%.) “The implications of undiagnosed or late diagnosis of valvular heart disease are dire and pose a significant cost to our health care system,” said lead author Moshe Rancier, MD, senior medical director of Mass General Brigham Community Physicians in Lawrence, Massachusetts. “This study demonstrates that health care professionals can screen patients for valvular heart disease more effectively and quickly using a digital stethoscope paired with high-performing AI that could detect cardiac murmurs associated with significant valvular heart disease.”

A second study used data from the UK Biobank to analyze the health records of approximately 500,000 adults (average age, 59 years; 45.5% female; 85.5% White), with the goal assessing the efficacy of using retinal images as analyzed by a deep-learning algorithm to predict the risk of CVD events, defined as heart attack, ischemic stroke, transient ischemic attack, or death due to heart attack or stroke.

The second analysis showed that after adjusting for CVD risk factors, such as age, gender, high blood pressure medication use, cholesterol medication use, and smoking history, the machine learning model was successful at identifying risk, as patients in the moderate-risk group were 57% more likely to experience a cardiovascular event than those in the low-risk group. Patients with high-risk scores were 88% more likely to experience a cardiovascular event compared with those in the low-risk group.

“These results show the potential of using AI analysis of retinal imaging as an early detection tool for heart disease in high-risk groups such as people who have prediabetes and type 2 diabetes,” said Chan Joo Lee, MD, PhD, the study’s lead author and an associate professor at Yonsei University in Seoul, Korea. “This could lead to early interventions and better management of these patient groups, ultimately reducing the incidence of heart disease-related complications.”