Cardiomyopathies occur when genetic or other health conditions weaken or damage heart muscles. Testing for these conditions relies on advanced diagnostic imaging tests, which can be expensive, hard to access, and require experts to interpret these tests, so they often go underdiagnosed. Now, researchers at the Cardiovascular Data Science (CarDS) Lab at Yale School of Medicine (YSM) have developed an algorithm to recognize signs of two common types of cardiomyopathies in ultrasound imaging captured during emergency room visits over the last decade across two health systems—with AI picking up signs of disease two years, on average, before the patients were formerly diagnosed.

vaxAI-based tests like this one could be deployed in emergency rooms to flag people at high risk of cardiomyopathies, says Evangelos Oikonomou, MD, DPhil, a clinical fellow in cardiovascular medicine and postdoctoral fellow in the CarDS Lab at YSM. We can actually minimize how many cases fall through the cracks, he says. Cardiomyopathies are far more common than we used to think. The Centers for Disease Control and Prevention (CDC) now estimates that as many as 1 in 500 people might have a form of cardiomyopathy. But even today, many people who live with the condition —or die of it—go undiagnosed.

Part of the problem is that getting tested for cardiomyopathies takes time and money. Because there is no routine screening or telltale symptoms, patients need to be in a situation where a medical professional recognizes that they have symptoms of the illness, such as shortness of breath or chest pains, before undergoing expensive testing. But there might be an easier way for doctors to flag that someone may be at risk for cardiomyopathy.

Ultrasounds for heart conditions usually last around 30 minutes. But people who visit emergency rooms with symptoms of heart distress sometimes receive cheaper, fast ultrasounds of their chests. These tests aren’t meant to pick up cardiomyopathy. But they might present an opportunity to screen for cardiomyopathy risk—not by asking overworked emergency room physicians to search for symptoms, but to have an algorithm scan information that is already acquired as part of clinical care.

Source: https://medicine.yale.edu/news-article/ai-can-identify-cardiomyopathies-myysm/