This AI program can predict heart attacks and stroke risks a decade from now using a single chest x-ray

This AI program can predict heart attacks and stroke risks a decade from now using a single chest x-ray

CHICAGO— A patient’s risk of heart attack or stroke over the next 10 years can be predicted from a single chest X-ray thanks to a new computer system.

Researchers from the Radiological Society of North America say it combines AI (artificial intelligence) with standard X-ray to find patterns associated with hardening of the arteries. This technique gives hope that doctors will be able to prescribe cholesterol-lowering drugs to vulnerable people before it is too late.

“Our deep learning model offers a potential solution for opportunistic population-based cardiovascular disease risk screening using existing chest X-ray images,” says study lead author Jakob Weiss, MD, radiologist affiliated with the Cardiovascular Imaging Research Center at Massachusetts General Hospital. and the AI ​​in Medicine program at Brigham and Women’s Hospital in Boston, in a press release.

“This type of screening could be used to identify people who would benefit from statins but who are currently untreated.”

Eating plenty of fish, fruits and vegetables and going for a brisk walk or bike ride are also protective habits that can reduce the risk of heart disease.

Deep learning is a complex series of algorithms that allow machines to make predictions based on patterns of data. The method, presented at the RSNA annual meeting in Chicago, could revolutionize cardiac therapy.

Who needs medicine for heart disease?

Current guidelines recommend estimating a patient’s 10-year risk to determine who should take statins for primary prevention. This is based on the ASCVD (atherosclerotic cardiovascular disease) risk score which takes into account a multitude of factors. They include age, gender, race, high blood pressure, smoking history, type 2 diabetes, and blood tests. Those who get 7.5% or more should be given statins.

“The variables needed to calculate the risk of ASCVD are often not available, making population-based screening approaches desirable,” says Dr. Weiss. “Because chest X-rays are commonly available, our approach can help identify those at high risk.”

Normal chest X-ray (CREDIT: Radiological Society of North America)

The US team trained the model, known as CXR-CVD risk, to predict death from cardiovascular disease using 147,497 chest X-rays from 40,643 participants in a cancer screening trial.

“We’ve known for a long time that X-rays capture information beyond traditional diagnostic results, but we didn’t use that data because we didn’t have robust and reliable methods,” Dr. Weiss continues. “Advances in AI make this possible now.”

In testing, the system accurately predicted heart attacks and strokes in a group that underwent routine chest X-rays at Mass General Brigham. About 10 percent of the 11,430 outpatients suffered a major cardiac event during the average follow-up of just over a decade. The system also identified those who were eligible for potentially life-saving statin therapy.

The new system takes advantage of one of the most common medical scanners

“The beauty of this approach is that all you need is an X-ray, which is acquired millions of times a day across the world,” the study’s author explains. “Based on a single existing chest x-ray image, our deep learning model predicts future major adverse cardiovascular events with similar performance and additional value to the established clinical standard.”

If a randomized, controlled trial validates the results, it could help doctors make the right treatment decisions.

“What we’ve shown is that a chest X-ray is more than a chest X-ray,” concludes Dr. Weiss. “With an approach like this, we get a quantitative measure, which allows us to provide both diagnostic and prognostic information that helps the clinician and the patient.”

Cardiovascular disease is the leading cause of death worldwide, killing nearly 18 million people each year.

South West News Service writer Mark Waghorn contributed to this report.

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