AI predicts future risk of heart disease using chest X-ray

AI predicts future risk of heart disease using chest X-ray

AI predicts future risk of heart disease using chest X-ray

Normal chest X-ray

Normal chest X-ray. Credit: Radiological Society of North America

Researchers have developed a deep learning model that uses a single chest X-ray to predict the 10-year risk of death from heart attack or stroke, which results from atherosclerotic cardiovascular disease. The results of the study were presented today (November 29) at the annual meeting of the Radiological Society of North America (RSNA).

Deep learning is an advanced type of artificial intelligence (AI) that can be trained to search X-ray images for patterns associated with disease.

“Our deep learning model offers a potential solution for population-based opportunistic cardiovascular disease risk screening using existing chest X-ray images,” said the study’s lead author, Jakob Weiss, MD, a radiologist affiliated with the Cardiovascular Imaging Research Center at Massachusetts General Hospital and the AI ​​Program at of medicine at Brigham and Women’s Hospital in Boston. “This type of screening could be used to identify individuals who would benefit from statins but are not currently receiving treatment.”

Current guidelines recommend assessing the 10-year risk of serious adverse cardiovascular events to determine who should receive a statin for primary prevention.

“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 over the established clinical standard.” — Jakob Weiss, MD

This risk is calculated using the Atherosclerotic Cardiovascular Disease (ASCVD) Risk Score, a statistical model that takes into account many variables, including age, sex, race, systolic blood pressure, hypertension treatment, smoking, type 2 diabetes and blood tests. Statin treatment is recommended for patients with a 10-year risk of 7.5% or greater.

“The variables needed to calculate ASCVD risk are often not available, making population-based screening approaches desirable,” Dr. Weiss said. “Because chest X-rays are generally available, our approach can help identify high-risk individuals.”

dr. Weiss and a team of researchers trained a deep learning model using a single chest X-ray (CXR) input. They developed a model, known as CXR-CVD risk, to predict the risk of death from cardiovascular disease using 147,497 chest X-rays from 40,643 participants in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial, a multicenter, randomized controlled trial that was designed and sponsored by the National Cancer Institute.

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

The researchers tested the model using a second independent cohort of 11,430 outpatients (mean age 60.1 years; 42.9% male) who had a routine outpatient chest X-ray at Mass General Brigham and were potentially eligible for statin therapy.

Of the 11,430 patients, 1,096 or 9.6% suffered a major adverse cardiac event during a median follow-up of 10.3 years. There was a significant association between risk predicted by the CXR-CVD deep learning risk model and observed major cardiac events.

The researchers also compared the model’s prognostic value to an established clinical standard for deciding on statin eligibility. This could be calculated in only 2401 patients (21%) due to missing data (eg blood pressure, cholesterol) in the electronic record. For this subgroup of patients, the CXR-CVD risk model was similar to the established clinical standard and even provided an incremental value.

“The beauty of this approach is that you only need an X-ray image, which is taken millions of times a day around the world,” said Dr. Weiss. “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 over the established clinical standard.”

dr. Weiss said more research, including a controlled, randomized trial, is needed to validate the deep learning model, which could ultimately serve as a decision support tool for doctors.

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

Co-authors Vineet Raghu, Ph.D., Kaavya Paruchuri, MD, Pradeep Natarajan, MD, MMSC, Hugo Aerts, Ph.D., and Michael T. Lu, MD, MPH The researchers were supported in part by funding from the National Academy of Medicine and the US heart association.

Meeting: 108th Scientific Assembly and Annual Meeting of the Radiological Society of North America



title_words_as_hashtags]

Leave a Comment

Your email address will not be published. Required fields are marked *