Introduction to Heart Disease Detection
The future of heart disease detection and prevention may have started with an opera singer. When Dr. Joshua Hutcheson heard about how his wife — then a grad student — was studying how subtle changes in the vocal chords could affect a singer’s voice, he started thinking about the human heart in a completely new way. He began to wonder: If trained ears can pick up imperceptible differences in voice, could artificial intelligence detect subtle changes in the sound of a heartbeat?
Background and Research
At Florida International University, Hutcheson is the director at the Center for Innovation in Cardiovascular Health, where he leads a lab of the university’s students and researchers to explore just that. He began this line of research in 2018, aiming to better understand why cardiovascular diseases develop and how to better detect them sooner. The team’s latest diagnostic approach combines digital stethoscopes and machine learning to analyze heartbeats, similar to how a musician may pick up an off-key note. According to the Centers for Disease Control and Prevention, heart disease remains the leading cause of death globally, often without symptoms until a serious event occurs.
A New Way to Listen
With heart sounds collected from the digital stethoscopes, these researchers feed that audio data into a computer for further analysis. “This would just give us another tool… then your doctor can work with you to then make subtle changes or put you on a treatment that really gives you a better quality of life moving forward rather than, say, waiting until you’ve had a major heart attack,” Hutcheson said. The goal is to train the machine learning or AI model to detect subtle changes in heartbeats that human ears may miss, offering the potential for earlier, preventative care. “ What we’re doing is taking that signal and using machine learning to try to analyze that,” Hutcheson explains. “To pick out very subtle changes that indicate the presence of disease.”
Health and AI Intersect
To train the algorithm, the team began with pre-clinical data from lab models, including collecting sounds from healthy and diseased mice hearts, with some data from human hearts as well. The sounds capture tiny mechanical vibrations, sometimes caused by stiffening arteries or narrowing heart vessels, that can indicate the early onset of disease. “We aren’t telling the computer what it is,” Hutcheson explains. “Then we look at how well it’s able to then predict whether it’s diseased or not — and we see about 95% accuracy on it.” It means the new model has the potential to catch those sounds that doctors at a regular check up sometimes miss, he explained. “ There’s studies out there suggesting that a doctor listening through a stethoscope can pick up the initial stages of disease, only about 30 to 40% of the time, so that’s a major improvement,” Hutcheson said.
Potential Applications and Impact
The model is being trained to recognize acoustic signatures of calcification— a condition where soft tissue in the arteries hardens into bone-like material, often with no previous symptoms. Hutcheson says the goal is to collect enough data and evidence to eventually move the algorithm into clinical use. That could mean a low-cost tool that patients use at home, transmitting data directly to their physician. “We think it could be something like a wearable blood pressure monitor,” Hutcheson said. “This could enable someone at home to take these measurements, have them transmitted to a system their doctor can access, and then regularly monitor it, whether it’s for diagnosis or for ongoing treatment.” That kind of access could have a real impact, Hutcheson says, especially for patients in communities where regular access to cardiologists or advanced imaging tools can be limited. “Anyone who doesn’t have adequate access,” Hutcheson said. “Whether that be in a rural community, or cannot afford regular access to healthcare, we think it can have a tremendous impact there.”
Beyond Just Cardiology
The research is supported by grants from the Florida Heart Research Foundation and Baptist Health Hospital in Kendall, and includes collaborations with institutions like the University of Miami and other clinical partners. While heart health is the current focus, Hutcheson believes this sound-based approach has a potential for growth and could extend well beyond cardiology. “ Anything that’s producing sound, that could become abnormal with changes, in the tissue, in the body, this could be used for, so I can certainly imagine, bone health, muscle health, anything like that,” he says. “I don’t see why it couldn’t be used.” Hutcheson says this convergence of sound science, artificial intelligence, and healthcare has the potential to open many new doors. “I think that’s really where the magic happens,” Hutcheson said. “When you can get these people together… working on a common problem and providing expertise across the board.”
Conclusion
The use of artificial intelligence to detect heart disease early is a promising area of research that could potentially save countless lives. With the ability to analyze heart sounds and detect subtle changes, this technology could provide earlier diagnosis and treatment, improving patient outcomes. As the research continues to evolve, it is likely that we will see new and innovative applications of this technology in the field of cardiology and beyond.
FAQs
Q: What is the current method of detecting heart disease?
A: Currently, heart disease is often detected through symptoms, which may not appear until a serious event occurs. Doctors may also use stethoscopes to listen to heart sounds, but this method has limitations.
Q: How does the new AI model work?
A: The AI model uses digital stethoscopes to collect heart sounds, which are then analyzed using machine learning algorithms to detect subtle changes that may indicate the presence of disease.
Q: What is the potential impact of this technology?
A: The potential impact of this technology is significant, as it could provide earlier diagnosis and treatment, improving patient outcomes and saving lives. It could also increase access to healthcare for patients in underserved communities.
Q: What are the potential applications of this technology beyond cardiology?
A: The potential applications of this technology beyond cardiology are vast, and could include the detection of diseases related to bone health, muscle health, and other areas where sound science and AI could be applied.
