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Updated: Aug 8, 2025

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
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Artificial intelligence and body composition.

Prasanna Santhanam1, Tanmay Nath2, Cheng Peng3

  • 1Division of Endocrinology, Diabetes, & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA.

Diabetes & Metabolic Syndrome
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) aids body composition analysis for predicting cardiometabolic health. AI techniques show promise in assessing muscle mass and cardiovascular risk, though bias mitigation is needed for broader application.

Keywords:
AI and Body composition

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Health

Background:

  • Obesity and normal BMI individuals can face metabolic disease risks due to visceral adiposity and sarcopenia.
  • Artificial Intelligence (AI) offers potential for advanced body composition assessment.
  • Predicting cardiometabolic health requires accurate body composition analysis.

Purpose of the Study:

  • To systematically review literature on AI techniques for body composition assessment.
  • To identify general trends in AI applications for body composition analysis.
  • To explore AI's role in predicting cardiometabolic health.

Main Methods:

  • Systematic literature search of Embase, Web of Science, and PubMed.
  • Inclusion of 51 studies after removing duplicates, irrelevant studies, and reviews.
  • Analysis of AI techniques applied to body composition assessment.

Main Results:

  • AI techniques are used with various imaging modalities (CT, MRI, ultrasonography, EKG) for body composition analysis.
  • Deep learning with convolutional networks enables automatic segmentation and quantification of muscle mass.
  • Identified limitations include population heterogeneity, sampling bias, and lack of generalizability.

Conclusions:

  • AI-assisted body composition measurement can enhance cardiovascular risk stratification.
  • Appropriate clinical context is crucial for effective AI application in risk assessment.
  • Further research is needed to address bias and improve AI generalizability for body composition analysis.