Deep learning-based biological age estimation from magnetic resonance imaging predicts cardiometabolic outcomes in the general population
Motivation: Little research has been done into how the body's anatomy can indicate aging. Goal(s): The goal of this study is to develop and validate new measures of biological age based on an image of an individual's internal anatomy. Approach: We use deep learning to estimate biological age from fat and muscle in abdominal MRI (we refer to as MRI-Age) Results: Deep learning-derived MRI-Age predicts cardiometabolic outcomes in the general population beyond chronological age, body composition measures, and cardiometabolic risk factors. Impact: Individuals at high MRI-Age could benefit from personalized prevention strategies, lifestyle interventions, and treatment planning.
Authors
- Susanne Rospleszcz (ORCID: https://orcid.org/0000-0002-4788-2341)
- Fabian Bamberg (ORCID: https://orcid.org/0000-0002-7460-3942)
- Michael T. Lu (ORCID: https://orcid.org/0000-0003-4696-9610)
- Marco Reisert (ORCID: https://orcid.org/0000-0003-2742-1940)
- Vineet K. Raghu (ORCID: https://orcid.org/0000-0003-3524-3945)
- Christopher L. Schlett (ORCID: https://orcid.org/0000-0002-1576-1481)
- Jakob Weiss (ORCID: https://orcid.org/0000-0002-0336-8519)
- Matthias Jung (ORCID: https://orcid.org/0000-0002-1124-4284)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/0990
- Primary Topic
- Health, Environment, Cognitive Aging
- Type
- article
- Field-Weighted Citation Impact
- 0.00