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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