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, resulting from atherosclerotic cardiovascular disease. The results of the study were presented today 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 screening of cardiovascular disease risk using existing chest X-ray images,” said lead study author Jakob Weiss, MD , a radiologist affiliated with the Massachusetts Cardiovascular Imaging Research Center. General Hospital and the AI in Medicine program at Brigham and Women’s Hospital in Boston. “This type of screening could be used to identify people who would benefit from statin medication but are currently untreated.”
Current guidelines recommend estimating the 10-year risk of major adverse cardiovascular events to establish who should receive a statin for primary prevention.
This risk is calculated using the atherosclerotic cardiovascular disease (ASCVD) risk score, a statistical model that considers a number of variables, including age, sex, race, systolic blood pressure, hypertension treatment, smoking, type 2 diabetes and blood tests. Statin medication 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 approaches to population-based screening desirable,” said Dr. Weiss. “Because chest X-rays are routinely available, our approach can help identify people at high risk.”
Dr. Weiss and a team of researchers trained a deep learning model using a single chest X-ray (CXR) input. They developed the model, known as CXR-CVD risk, to predict the risk of death from cardiovascular disease using 147,497 chest radiographs from 40,643 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, a multicenter, randomized controlled trial designed and sponsored by the National Cancer Institute.
We have long recognized that X-rays capture information beyond traditional diagnostic findings, but we have not used this data because we lack robust and reliable methods. Advances in AI make that possible now.”
Dr. Jakob Weiss, MD, radiologist, lead author of the study
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 the statin therapy.
Of the 11,430 patients, 1,096, or 9.6%, experienced 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 risk deep learning model and observed major cardiac events.
The researchers also compared the model’s prognostic value to the established clinical standard for deciding statin eligibility. This could only be calculated in 2401 patients (21%) due to missing data (eg blood pressure, cholesterol) in the electronic record. For this subset of patients, the CXR-CVD risk model performed similarly to the established clinical standard and even provided incremental value.
“The beauty of this approach is that you only need one X-ray, which is acquired 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 to the established clinical standard.”
Dr. Weiss said additional research, including a randomized controlled trial, is needed to validate the deep learning model, which could ultimately serve as a decision support tool for treating physicians.
“What we’ve shown is that a chest X-ray is more than just a chest X-ray,” Dr. Weiss said. “With an approach like this, we get a quantitative measure, which allows us to provide both diagnostic and prognostic information that helps the doctor and the patient.”
Co-authors are 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 American Heart Association.
Source:
Radiological Society of North America