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Abstract #1289

Biological age assessment in 100,000 whole-body MRI of the German National Cohort (NAKO) and UK Biobank

Veronika Ecker1,2, Bin Yang2, Sergios Gatidis1, and Thomas Küstner1
1Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany, 2Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany

Synopsis

Keywords: Analysis/Processing, Aging, Biological Age, Machine Learning/Artificial Intelligence, Data Analysis

Motivation: MRI provides valuable insights into aging, yet quantifying the true biological age is challenging. Investigations in large, multi-cohort studies can provide comprehensive insights into aging patterns and their influencing factors.

Goal(s): This study aims to estimate biological across multiple organs using MRI data from large, multi-cohort studies.

Approach: We used ResNet-based models on MRI data from 100,000 participants across two cohorts (UK Biobank and NAKO) to predict organ-specific biological age.

Results: The results show that biological age can be estimated for various organs in both cohorts. GradCAM analysis highlights significant regions consistent with known age-related changes.

Impact: Our imaging-based multi-organ prediction of biological age from whole-body MRI of 100,000 participants in the UK Biobank and NAKO cohorts provides an important foundation to investigate aging patterns and influencing factors.

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