Health

This AI program can predict heart attack and stroke risk within a decade using a single chest X-ray

This AI program can predict heart attack and stroke risk within a decade using a single chest X-ray

CHICAGO — A patient’s risk of heart attack or stroke in the next 10 years can be predicted from just one chest X-ray using a new computer system.

Researchers at the Radiological Society of North America say it combines AI (artificial intelligence) with standard X-rays to find patterns associated with hardening of the arteries. The technique offers 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 population-based opportunistic cardiovascular disease risk screening using existing chest X-rays,” says lead study author Jakob Weiss, MD, a radiologist affiliated with the Center for Cardiovascular Imaging Research at Massachusetts General Hospital. and the AI ​​in Medicine program at Brigham and Women’s Hospital in Boston, ua press release.

“This type of screening could be used to identify individuals who would benefit from statins but they are currently not being treated.”

Eating plenty of fish, fruits and vegetables, brisk walking or cycling 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 in data. The method, presented at annual meeting RSNA in Chicago, could revolutionize heart therapy.

Who needs drugs for heart disease?

Current guidelines recommend assessing a patient’s 10-year risk to determine who should take statins for primary prevention. This is based on an ASCVD (atherosclerotic cardiovascular disease) risk score that takes into account a number of factors. These include age, gender, race, high blood pressure, smoking history, type 2 diabetes and blood tests. Those with 7.5 percent or more should receive statins.

“The variables needed to calculate ASCVD risk 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 high-risk individuals.”

A 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 diseases using 147,497 chest X-rays from 40,643 participants in a cancer screening trial.

“We’ve long recognized that X-rays capture information beyond traditional diagnostic findings, but we didn’t use that data because we didn’t have robust, reliable methods,” continues Dr. Weiss. “Advances in AI now they make it possible.”

In tests, the system accurately predicted heart attacks and strokes in a group who had routine chest X-rays at Mass General Brigham. About 10 percent of the 11,430 outpatients suffered a major cardiac event during an 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 scans

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

If a controlled, randomized trial confirms the results, it could support doctors in the design correct treatment decisions.

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

Cardiovascular diseases are the world’s number one killer – claiming almost 18 million lives a year.

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





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