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CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence

B Dustin Pooler¹, John W Garrett¹, Matthew H Lee¹

¹Department of Radiology, University of Wisconsin School of Medicine & Public Health, E3/311 Clinical Science Center, 600 Highland Ave, Madison, WI 53792-3252.

AJR. American Journal of Roentgenology

|January 8, 2025

Summary

Age, sex, and systemic diseases significantly impact body composition measurements derived from CT scans using artificial intelligence (AI). Understanding these associations is crucial for developing AI-driven clinical tools for body composition analysis.

Keywords:

CT abdomen artificial intelligence body composition reference range

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Computed tomography (CT)-based body composition analysis is linked to health outcomes.
Artificial intelligence (AI) enables automated body composition measurement in large patient cohorts.
Understanding demographic and disease-related variations in body composition is essential.

Purpose of the Study:

To evaluate the associations between age, sex, and common systemic diseases with CT-derived body composition metrics.
To assess the performance of automated AI tools in quantifying body composition parameters.
To establish a foundation for normative reference ranges in AI-based body composition analysis.

Main Methods:

Retrospective analysis of 140,606 adult abdominal CT scans.
Application of 13 automated AI tools for quantifying liver, spleen, kidney, vertebral, muscle, and fat composition.
Electronic health record review to identify systemic diseases like cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis.

Main Results:

Age, sex, and systemic diseases were significant predictors for all 13 body composition measures (p < .001).
Age predicted all measures; sex predicted 12; cancer predicted 9; CVD predicted 11; DM predicted 13; cirrhosis predicted 12.
Models showed variable goodness of fit (R² = 0.03-0.43).

Conclusions:

Demographic factors (age, sex) and common systemic diseases are significant predictors of AI-derived CT body composition measures.
These findings are critical for developing clinical reference ranges for AI-based body composition tools.
AI tools offer a scalable approach to body composition assessment in diverse patient populations.