Deep Learning and Total-Body DXA Scans (TBDXA.I.)

Status: Completed

Whole-body DXA scan

This research applies deep-learning methods to total-body DXA (dual-energy X-ray absorptiometry) scans to extract predictive information about body composition and health outcomes. The project uses self-supervised learning on whole-body images from the Health, Aging and Body Composition (Health ABC) Study, which includes over 3,000 participants with follow-up scans at years 3, 6, and 10.

The approach generates algorithms to predict clinical outcomes — cardiovascular disease, mortality, cancer, hip fracture, physical disability, diabetes-related measures — extracting features from unlabeled DXA images without requiring labeled outcome data during the initial learning phase.

Specific aims

Predict clinical endpoints — generate predictive algorithms for CVD, CVD death, overall mortality, cancer, cancer death, hip fracture, physical disability, incident insulin-resistant diabetes, and metabolic markers by applying deep learning to baseline and follow-up Total-Body DXA images.
Predict physical performance and inflammation — algorithms for gait speed, corridor walk speed, and IL-6 concentration.
Explain the model — explore image features accounting for algorithm accuracy using saliency mapping techniques.

Research team

Principal Investigators

Steve Cummings, MD, FAPC — San Francisco Coordinating Center
John Shepherd, PhD — UH Cancer Center

Co-Investigator

Peter Sadowski, PhD — UH Mānoa, Information and Computer Sciences

Additional team members

Warren Browner, MD, MPH — CEO, California Pacific Medical Center
Eleanor Simonsick, PhD — Epidemiologist, National Institute on Aging (NIH)
Yannik Glaser — Graduate Student, UH Mānoa ICS
Lily Liu — Statistician, California Pacific Medical Center

Funding

Sutter Health / California Medical Center Research Institute — Grant 2805096-0100 · 11/01/2019 – 10/31/2020.

Key publication

Glaser Y, Shepherd J, Leong L, Wolfgruber T, Lui L-Y, Sadowski P, et al. Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging. Communications Medicine 2022 Aug 16;2. DOI: 10.1038/s43856-022-00166-9

The strongest model achieved an area under the ROC curve of 0.79 on held-out test data from over 15,000 scans. The study employed explainable-AI techniques to interpret predictions and evaluate input contributions.

Presentation

Glaser Y, Sadowski P, Wolfgruber T, Lui L-Y, Cummings S, Shepherd J. Hip fracture risk modelling using DXA and artificial intelligence. Poster, American Society for Bone Mineral and Research Annual Meeting; 2020 Sep 11; Virtual.

Products & Dissertations

Yannik Glaser, PhD (2022). Deep Learning for Medical Image Analysis in Whole-Body DXA and Mammography. Doctoral dissertation, Department of Information and Computer Sciences, University of Hawaiʻi at Mānoa.
Lambert Leong, PhD (2021). Deep Learning and Computer Vision Approaches for Medical Image Analysis in Oncology and Body Composition. Doctoral dissertation, University of Hawaiʻi at Mānoa.