Heart disease is still the number one cause of death around the world. It often develops slowly over many years without clear warning signs.
By the time symptoms appear, the disease may already be advanced. Because of this, doctors are always looking for better ways to identify risk earlier so they can prevent heart attacks, strokes, and other serious problems.
A new study from Mayo Clinic offers an exciting step forward. Researchers have found a way to use artificial intelligence, or AI, to improve how doctors predict long-term heart disease risk. The findings were presented at the 2026 American College of Cardiology Scientific Session and were also published in the American Journal of Preventive Cardiology.
The study followed nearly 12,000 adults for about 16 years. This long follow-up allowed researchers to see who eventually developed heart disease and who did not.
The team focused on a common imaging test called a coronary artery calcium scan. This scan is already widely used to check for calcium buildup in the arteries, which is a sign of heart disease.
Instead of adding a new test, the researchers used AI to get more information from this existing scan. Specifically, they measured the amount of fat around the heart, known as pericardial fat. In the past, this fat was difficult to measure accurately and was not commonly used in routine care.
The results showed that this fat measurement is very important. People with more fat around their heart had a higher risk of developing heart disease. This was true even after taking into account traditional risk factors such as age, blood pressure, cholesterol, and diabetes.
About 10% of participants developed heart disease during the study. Those with the highest levels of heart fat had a much greater risk, regardless of how much calcium was found in their arteries. This means that even people who appear low risk based on standard tests may still have hidden risk that can now be detected.
The researchers also compared this new method with existing tools. One common tool is the PREVENT equation, which estimates risk based on personal health factors. Another is the calcium score from the scan itself.
When the new fat measurement was added, the accuracy of risk prediction improved significantly, especially for people in the low or borderline risk groups.
This is important because doctors often find it hardest to make decisions for patients in these middle categories. Some people may not need treatment, while others might benefit from early action. Better information can help doctors decide who needs more attention and who does not.
Another advantage of this method is that it does not require extra tests or costs. Since many patients already undergo calcium scans, AI can simply analyze the same images to provide additional insights. This makes it a practical and scalable approach for healthcare systems.
However, the study also has limitations. While the results are promising, more research is needed to confirm how this method should be used in everyday practice. It is not yet clear how doctors should change treatment decisions based on this new measurement.
Overall, this study shows how AI can unlock new information from existing medical tests. It highlights a shift toward more personalized care, where doctors can better understand each patient’s unique risk.
In the future, tools like this may help identify heart disease earlier and allow for more targeted prevention. This could lead to better outcomes and save many lives.
If you care about heart health, please read studies about how eating eggs can help reduce heart disease risk, and Vitamin K2 could help reduce heart disease risk.
For more information about heart health, please see recent studies about how to remove plaques that cause heart attacks, and results showing a new way to prevent heart attacks, strokes.
Source: Mayo Clinic.
