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Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
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Longitudinal evaluation of intra-patient changes in computed tomography-based body composition measures.

B Dustin Pooler1, John W Garrett2, Matthew H Lee2

  • 1School of Medicine and Public Health, University of Wisconsin-Madison, Madison, USA. bpooler@uwhealth.org.

Abdominal Radiology (New York)
|March 5, 2026
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Summary

Longitudinal body composition changes significantly differ between sexes and age groups. Automated AI tools reveal age and sex-specific variations in CT-measured body composition over time.

Keywords:
AbdomenArtificial intelligence (AI)Body compositionComputed tomography (CT)

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Body composition changes with age and sex.
  • Accurate assessment of these changes is crucial for health monitoring.
  • Automated tools offer potential for efficient analysis of body composition.

Purpose of the Study:

  • To analyze longitudinal, intra-patient changes in CT-based body composition.
  • To utilize fully automated artificial intelligence (AI) tools for body composition assessment.
  • To investigate age and sex as predictors of these changes.

Main Methods:

  • Retrospective study of 15,616 adult patients with at least two abdominal CT scans.
  • AI tools quantified vertebral trabecular attenuation, skeletal muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT).
  • Longitudinal changes were analyzed over a mean interval of 9.1 years, with statistical analysis for age and sex predictors.

Main Results:

  • Significant sex-specific differences in body composition change rates were observed for most measures (p < 0.05), excluding muscle attenuation.
  • Age predicted body composition measures, though with small effect sizes (R² 0.002-0.040), varying across age groups.
  • Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) changes showed distinct patterns influenced by age and sex.

Conclusions:

  • Longitudinal body composition changes exhibit significant age- and sex-specific variations.
  • Automated AI analysis provides valuable insights into intra-patient body composition dynamics over time.
  • Findings highlight the importance of considering demographic factors in interpreting body composition data.