Deep Learning to Estimate Biological Age From Chest Radiographs.

ABSTRACT:

Objectives

The goal of this study was to assess whether a deep learning estimate of age from a chest radiograph image (CXR-Age) can predict longevity beyond chronological age.

Background

Chronological age is an imperfect measure of longevity. Biological age, a measure of overall health, may improve personalized care. This paper proposes a new way to estimate biological age using a convolutional neural network that takes as input a CXR image and outputs a chest x-ray age (in years) as a measure of long-term mortality risk.

Methods

CXR-Age was developed using CXR from 116,035 individuals and validated in 2 held-out testing sets: 1) 75% of the CXR arm of PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial) (N = 40,967); and 2) the CXR arm of NLST (Nation

SUBMITTER: Raghu VK 

PROVIDER: S-EPMC11381093 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature