It is hard to turn on or read the news without the topic of Artificial Intelligence (AI) cropping up – it seems to be everywhere. Some of the AI news trends to doom and gloom with AI taking over the world, and Hollywood definitely jumping on that bandwagon to stoke the fires. However, AI, and the subfield of deep learning, have real positive and tangible benefits for the healthcare world. AI is already being used in healthcare to improve diagnosis, treatment, and patient outcomes. And now, AI may be able to help detect heart valve disease and predict the risk of cardiovascular events according to two studies presented at the American Heart Association’s Scientific Sessions 2023.
The American Heart Association’s Scientific Sessions annual meeting is a premier global exchange of the latest scientific advancements, research and evidence-based clinical practice updates in cardiovascular science. At the 2023 Scientific Sessions held Nov. 11-13, 2023 two preliminary research studies were presented utilizing AI and deep learning models to detect heart valve disease and predict cardiovascular risk.
Real World Evaluation of an Artificial Intelligence Enabled Digital Stethoscope for Detecting Undiagnosed Valvular Heart Disease in Primary Care (Abstract 306)
The first study sought to compare the ability of a primary care professional to detect potential heart valve disease using a standard stethoscope versus that of an AI program using sound data taken from a digital stethoscope to do the same.
AI refers to the broad field of computer science dedicated to creating machines or systems that can perform tasks that typically require human intelligence. AI systems are designed to learn from experience, improve performance, and adapt to new situations without being explicitly programmed. The tasks performed by AI may include problem-solving, learning, understanding natural language, speech recognition, and decision-making. The ultimate goal of AI is to create machines that can simulate human intelligence, including reasoning, problem-solving, and creativity.
The study comprised of 369 adults without a prior diagnosis of heart valve disease or a history of heart murmurs who received primary care at clinics in Queens, New York, and Lawrence and Haverhill, Massachusetts. Heart valve disease is a common condition that affects millions of people worldwide. It can lead to serious complications, including heart failure, stroke, and even death.
The analysis found:
The AI method with the digital stethoscope detected 94.1% of cases of valvular heart disease compared to the standard stethoscope used by primary care professionals, which detected only 41.2% of cases.
The AI method identified 22 people with previously undiagnosed moderate-or-greater heart valve disease, and the professionals using the standard stethoscopes identified 8 previously undiagnosed people with heart valve disease.
Deep Learning-Based Retinal Imaging for Predicting Cardiovascular Disease Events in Prediabetic and Diabetic Patients: A Study Using the UK Biobank (Abstract Poster Mo3070)
The second study utilized a deep learning model in determining patients’ risk of cardiovascular disease events by evaluating eye images of people with prediabetes and Type 2 diabetes.
Deep learning is a specific subfield within machine learning that involves the use of neural networks with multiple layers (deep neural networks) to model and solve complex problems. These networks are inspired by the structure and function of the human brain. Deep learning models can identify complex patterns in data such as text, pictures, and sounds to produce predictions and insights.
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. The researchers defined cardiovascular disease events as heart attack, ischemic stroke, transient ischemic attack, or death due to heart attack or stroke.
The research team provided the deep learning tool with retinal images of 1,101 people with prediabetes or Type 2 diabetes and asked the model to categorize each individual into low-risk, moderate-risk, and high-risk groups based on likelihood of cardiovascular disease. Subsequently, the participants were tracked for approximately 11 years for the number of cardiovascular disease events they experienced.
The analysis found:
8.2% of participants in the low-risk group, 15.2% of participants in the moderate-risk group and 18.5% of participants in the high-risk group had experienced cardiovascular disease events by the end of the study period of 11 years.
After accounting for demographic and other potential CVD risk factors, such as age, gender, high blood pressure medication use, cholesterol medication use and smoking history, people in the moderate-risk group were 57% more likely to experience a cardiovascular event compared to people in the low-risk group; and people with high-risk scores were 88% more likely to experience a cardiovascular event compared to those in the low-risk group.
Study lead author Chan Joo Lee, M.D., Ph.D., an associate professor at Yonsei University in Seoul, Korea, concluded “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.”
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