Overview
Deep Learning trains neural networks on large datasets, including images, to generate algorithms that predict endpoints. Total Body DXA scans contain digital information about body composition (fat, bone, lean tissue) and its distribution and body shape. Each of these characteristics (bone mass, fat mass, and lean mass) has been shown to be individually predictive of clinical outcomes associated with aging.
In this project, we attempt to use deep learning methods on total body DXA scans to extract more information than was previously done, and thus, providing more accurate predictions of clinical outcomes, including cardiovascular disease (CVD), CVD death, overall mortality, cancer, cancer death, hip fracture, physical disability, incident insulin-resistant diabetes, and severity of insulin resistance.
We will use a novel new approach called self-supervised learning to extract features from the DXA whole body images collected in the Health, Aging and Body Composition (Health ABC) Study. This dataset includes more than 3,000 participants, along with their follow-up examinations in years 3, 6, and 10. This learning process does not use the aging outcomes, just the “unlabeled” images themselves. It will create a penultimate set of features found in the DXA images that can be used in the neural network or outside it to predict the outcomes of interests.
Objective/Aims
The focus of this project is to apply deep learning methods to baseline and follow-up total Body DXA images to predict outcomes associated with age-related changes in body composition.
Our specific aims are:
- Generate predictive algorithms for CVD, CVD death, overall mortality, major disease-free mortality, cancer, cancer death, hip fracture, physical disability, incident insulin-resistant diabetes, and hemoglobin A1c, and fasting insulin and glucose levels by applying deep learning methods to baseline and follow-up Total Body DXA images.
- Generate predictive algorithms for physical performance including gait speed and long-distance corridor walk speed and levels of inflammation (eg. IL-6 concentration).
- Explore features of TBSXA images that account for the predictive accuracy of the algorithms by using saliency mapping.
Research Team
Steve Cummings, MD, FAPC
Principal Investigator
SF Coordinating Center
John Shepherd, PhD
Principal Investigator
UH Cancer Center
Peter Sadowski, PhD
Co-Investigator
UH Manoa Information and Computer Sciences
Warren Browner, MD, MPH
CEO
California Pacific Medical Center
Eleanor Simonsick, PhD
Epidemiologist
National Institute on Aging – NIH
Yannik Glaser
Graduate Student
UH Manoa Information and Computer Sciences
Lily Liu
Statistician
California Pacific Medical Center
Funding Source(s)
Sutter Health/California Medical Center Research Institute
2805096-0100
11/01/2019 – 10/31/2020
