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About this Publication
Title
Deep Learning to Estimate Biological Age From Chest Radiographs.
Pubmed ID
33744131 (View this publication on the PubMed website)
Digital Object Identifier
Publication
JACC Cardiovasc Imaging. 2021 Mar 10
Authors
Raghu VK, Weiss J, Hoffmann U, Aerts HJWL, Lu MT
Affiliations
  • Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Program for Artificial Intelligence in Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. Electronic address: vraghu@mgh.harvard.edu.
  • Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Program for Artificial Intelligence in Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Department for Diagnostic and Interventional Radiology, University Hospital Freiburg, Germany.
  • Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Program for Artificial Intelligence in Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.
  • Cardiovascular Imaging Research Center, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Program for Artificial Intelligence in Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CARIM & GROW, Department of Radiology and Nuclear Medicine, Maastricht University, Maastricht, the Netherlands.
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 (National Lung Screening Trial) (N = 5,414). CXR-Age was compared to chronological age and a multivariable regression model of chronological age, risk factors, and radiograph findings to predict all-cause and cardiovascular mortality with a maximum 23 years and 13 years of follow-up, respectively. The primary outcome was observed mortality; results are provided for the testing datasets only.

RESULTS: In the PLCO testing dataset, a 5-year increase in CXR-Age carried a higher risk of all-cause mortality than a 5-year increase in chronological age (CXR-Age hazard ratio [HR]: 2.26 [95% confidence interval (CI): 2.24 to 2.29] vs. chronological age HR: 1.77 [95% CI: 1.75 to 1.78]; p < 0.001). A similar pattern was found for cardiovascular mortality (CXR-Age cause-specific HR: 2.45 per 5 years [95% CI: 2.34 to 2.56] vs. chronological age HR: 1.82 per 5 years [95% CI: 1.74 to 1.90]). Similar results were seen for both outcomes in the NLST external testing dataset. Adding CXR-Age to the multivariable model resulted in significant improvements for predicting both outcomes in both testing datasets (p < 0.001 for all comparisons).

CONCLUSIONS: Based on a CXR image, CXR-Age predicted long-term all-cause and cardiovascular mortality.

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