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Abstract #0990

Deep learning-based biological age estimation from magnetic resonance imaging predicts cardiometabolic outcomes in the general population

Matthias Jung1,2, Marco Reisert3, Susanne Rospleszcz2, Christopher L Schlett2, Michael T Lu1, Fabian Bamberg2, Vineet K Raghu1, and Jakob Weiss2
1Radiology, Massachusetts General Hospital, Boston, MA, United States, 2Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany, 3Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Freiburg, Germany

Synopsis

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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Keywords