Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Cardiovascular, Analysis/Processing, Biomarkers
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.
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