Catalyst Awardee

Project Description

Deep Learning to Predict Biological Age and Longevity from Chest Radiographs

Vineet Raghu, PhD, Massachusetts General Hospital; Jakob Weiss, MD, Dana-Farber Cancer Institute; and Michael Lu, MD, MPH, Massachusetts General Hospital

Competition Sponsor: U.S. National Academy of Medicine

Age-related chronic disease causes 60% of deaths in the US. Primary prevention (e.g. statin to prevent cardiovascular disease) and screening (e.g. screening for lung cancer with chest CT) interventions are based on chronological age, but we know that chronological age is an imperfect measure of the aging process. A more accurate measure of biological age would enable healthcare providers to better personalize care and help researchers address factors underlying the aging process. The goal of our project is to develop a pragmatic measure of biological age using a convolutional neural network (CNN) and a chest x-ray image. We will evaluate whether this chest x-ray age predicts longevity and age-related disease better than chronological age. Chest x-rays are among the most common tests in medicine and leveraging these existing x-rays to more accurately assess biological age has the potential to transform care of the aging population. The CNN will be developed and tested using over 30,000 publicly available chest x-rays as well as up to 18-year follow-up for mortality in 60,000 individuals from two large multi-center clinical trials, the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial and the National Lung Screening Trial (NLST).

To learn more about this proposal, email healthylongevity@nas.edu.

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