An artificial intelligence (AI) model can predict your risk of dying from a heart attack or stroke over a 10-year period from just a single chest X-ray. Researchers trained the deep learning AI to search the X-ray images for patterns associated with atherosclerosis, the dominant cause of cardiovascular heart disease. Current health guidelines in the US recommend estimating a 10-year risk of major heart disease events, so that preventative measures can be taken where necessary, such as the use of statins. The risk is calculated using a score based of variables such as age, sex, race, blood pressure, hypertension treatment, smoking, Type 2 diabetes and blood tests. Statins are recommended for patients who have a 10-year risk of 7.5 percent or higher. "The variables necessary to calculate ASCVD risk are often not available, which makes approaches for population-based screening desirable," said the study's lead author, Dr Jakob Weiss, a radiologist affiliated with the Cardiovascular Imaging Research Center at Massachusetts General Hospital and the AI in Medicine program at the Brigham and Women's Hospital in Boston. "Our deep learning model offers a potential solution for population-based opportunistic screening of cardiovascular disease risk using existing chest X-ray images. "As chest X-rays are commonly available, our approach may help identify individuals at high risk. This type of screening could be used to identify individuals who would benefit from statin medication but are currently untreated." The team of researchers trained a deep learning model using 147,497 chest X-rays from 40,643 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, a multi-center, randomized controlled trial designed and sponsored by the National Cancer Institute in the US. They tested the model, called CXR-CVD risk, using a second independent cohort of 11,430 outpatients who had chest X-rays and were potentially eligible for statin therapy. Of 11,430 patients, 1,096 — or 9.6 percent — suffered a major adverse cardiac event over the median follow-up of 10.3 years. They found a "significant association" between the risk predicted by the CXR-CVD risk model and the actual observed major cardiac events. "The beauty of this approach is you only need an X-ray, which is acquired millions of times a day across the world," Weiss said. "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". He said that X-rays have long been known to capture information beyond traditional diagnostic findings, but that the data hasn't been used because "we haven't had robust, reliable methods". "Advances in AI are making it possible now," he said. "What we've shown is a chest X-ray is more than a chest X-ray. 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". Weiss said additional research, including a controlled, randomized trial, is necessary to validate the deep learning model, which could ultimately serve as a decision-support tool for treating physicians. Results of the study were presented on Tuesday at the annual meeting of the Radiological Society of North America (RSNA). — Euronews
