New AI Tool Identifies Risk of Future Heart Failure
In a new study led by Yale School of Medicine’s Cardiovascular Data Science (CarDS) Lab, researchers developed an artificial intelligence (AI) tool that can identify individuals at high risk of developing heart failure using electrocardiogram (ECG) images. The new tool enables earlier identification of heart failure, potentially reducing hospitalizations and premature death, the researchers said.
The study was published online in the European Heart Journal.
ECGs are noninvasive tests that measure the heart's electrical activity through electrodes placed on the skin. Since the tests are routinely performed and widely available, they provide an ideal platform for broader heart failure screening. Heart failure is a common cardiovascular disorder affecting millions of people worldwide.
Currently, identifying individuals at high risk for heart failure often relies on a series of clinical evaluations, including extensive clinical history, physical examination, and blood testing that may not be accessible in all settings, explains first author Lovedeep Singh Dhingra, MBBS, a postdoctoral fellow in the CarDS Lab.
The AI-based tool represents a paradigm shift in heart failure risk stratification, he said.
“Using an image or photograph of a 12-lead ECG as its input, our model was able to accurately stratify heart failure risk across diverse populations in the United States, United Kingdom, and Brazil,” Dhingra said. “We can now predict who is at risk of developing heart failure in the future, well before they show overt symptoms.”
The study’s senior author Rohan Khera, MD, MS, assistant professor of medicine (cardiovascular medicine) and director of the CarDS Lab, emphasized the potential public health impact of the work.
“Every time a clinician obtains an ECG—a test that is already part of standard clinical care—our simple tool now offers an opportunity for screening and risk stratification for cardiovascular disease,” he said. “The broad availability of ECG technology, even where resources are limited, enables early intervention and improved outcomes for patients who might otherwise go undiagnosed.”
As part of CarDS Lab’s global focus, the AI model was validated across multiple international populations, showcasing its potential for large-scale adoption.
“We want to ensure broad and equitable implementation of AI-based health technologies in everyday practice,” Khera said. “That’s our next frontier.”
This study was funded by grants from the National Heart, Lung, and Blood Institute, the National Institute on Aging of the National Institutes of Health (NIH), as well as by the Doris Duke Charitable Foundation.
Other Yale authors include: Arya Aminorroaya, MD, PhD, Veer Sangha, Aline Pedroso, PhD, Harlan Krumholz, MD, SM, and Evangelos Oikonomou, MD, DPhil.
To learn more, read the article: “Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.”
The Department of Internal Medicine at Yale School of Medicine is among the nation's premier departments, bringing together an elite cadre of clinicians, investigators, educators, and staff in one of the world's top medical schools. To learn more, visit Internal Medicine.
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