November 6, 2023

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AI may accurately detect heart valve disease and predict cardiovascular risk

Credit: CC0 Public Domain
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Credit: CC0 Public Domain

Advances in artificial intelligence have enabled the development and application of AI tools that may be effective at detecting heart valvular disease and predicting the risk of cardiovascular disease events, according to preliminary research in two studies to be presented at the American Heart Association's Scientific Sessions 2023, held Nov. 11–13, in Philadelphia.

"Computational methods to develop novel predictors of health and —'artificial intelligence"—are becoming increasingly sophisticated," said Dan Roden, M.D., FAHA, professor of medicine, pharmacology and biomedical informatics and senior vice-president for personalized medicine at Vanderbilt University Medical Center, as well as chair of the Association's Council on Genomic and Precision Medicine. "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."

Real world evaluation of an artificial intelligence enabled digital stethoscope for detecting undiagnosed valvular heart disease in primary care

A study conducted at three different primary care clinics in the U.S. compared the ability of a medical professional using a standard stethoscope to detect potential heart valve disease vs. the ability of an artificial intelligence program using sound data from a digital stethoscope to do the same.

Each study participant had a physical exam that included a primary care professional, either a physician or nurse practitioner, listening to their heart and lungs with a traditional stethoscope for unusual sounds or murmurs plus an exam with a digital stethoscope that recorded heart sounds. All participants also received an echocardiogram at a follow-up appointment one to two weeks later to determine if heart valve disease was present, though the results were not shared with the clinician or the patient.

The analysis found:

"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, M.D., 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."

The study's limitations include the small sample size of the study group, which prevents analyses of differences between subsets of participants (based on characteristics such as gender, race, ethnicity and age).

Additionally, while the AI method had greater sensitivity to sounds detected with the digital stethoscope, medical professionals using a standard stethoscope were able to be more specific in their diagnosis, 95.5% versus 84.5% for the AI method, which may reduce the potential for false positives and/or additional tests or screenings for valvular heart disease.

However, this study only evaluated the accuracy of the digital stethoscope in comparison to a traditional stethoscope. Rancier noted researchers plan to evaluate six-month patient follow-up data to review more closely the clinical outcomes and additional diagnostic tests and treatments.

Study background and details:

"We saw here that the AI-based stethoscope did extraordinarily well, it predicted nearly 90% of the of the valve disease diagnoses that were ultimately there. I see that as an emerging technology—using an AI-enabled stethoscope and perhaps combining it with other imaging modalities, like an AI-enabled echocardiogram built into your stethoscope," Roden said. "Use of these new tools to detect the presence of valvular disease as well as the extent of valvular disease and the extent of other kinds of heart disease will likely help to transform CVD care."

Deep learning-based retinal imaging for predicting cardiovascular disease events in prediabetic and diabetic patients: a study using the UK biobank

Using data from the UK Biobank, a second study by another research group evaluated the effectiveness of using pictures of the retina at the back of the eye that were analyzed by a deep-learning algorithm tool to predict the risk of cardiovascular disease events, defined as heart attack, ischemic stroke, transient ischemic attack or death due to heart attack or stroke.

Deep learning is a method of that trains computers to analyze multiple layers of data and gives computers the ability to "learn" by evolving their model independent of human intervention based on new information presented to it—a process challenged by the requirement of both large amounts of computing power and data.

Previous research had successfully developed a deep learning algorithm to predict cardiovascular disease events by analysis of retinal images and coronary artery calcium scores.

Researchers used the deep-learning algorithm to categorize retinal images of 1,101 people with prediabetes or type 2 diabetes into low-risk, moderate-risk and high-risk groups for likelihood of cardiovascular disease. They then measured the number of cardiovascular disease events among participants over a median period of 11 years.

The analysis found:

"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 study lead author Chan Joo Lee, M.D., Ph.D., 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 disease-related complications."

Study background and details:

The researchers tested the ability of the imaging to predict cardiovascular disease using a large dataset of people, however, this population was noted as primarily white race, which means the researchers' findings may not be applicable to other populations. Additional follow-up studies among people from diverse races and ethnicities are needed.

"These systems learn from big data sets, and they only learn from the data we give them to learn from. In the UK Biobank, for example, 93% of the participants are of European ancestry, so we have no sense of whether the approaches derived in the UK Biobank are relevant or meaningful to people who are not of European ancestry," Roden said.

"Another question is: does the retinal scan do a better job of predicting coronary artery disease than the pooled risk equations, or a polygenic risk score for coronary artery disease, or coronary calcium measurements? Those are all questions that need to be answered because as we develop more tools to predict events like coronary artery disease, we want to make sure that we use the right ones and the right combinations, rather than complicating care with alternate tools that have not been validated."

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