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Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
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FocusedON-BC: A Robust Deep Learning Framework for Automated Body Composition Assessment.

Jano Manuel Rubio-García1, Andrés Jiménez-Sánchez2, Fiorella Palmas3,4

  • 1Department of Radiology, Complejo Hospitalario Universitario Insular Materno Infantil de Canarias, 35016 Las Palmas de Gran Canaria, Spain.

Nutrients
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning tool, FocusedON-BC, automates body composition analysis from CT scans. It accurately quantifies skeletal muscle (SM) and adipose tissue (VAT, SAT), aiding routine nutritional screening.

Keywords:
body compositioncomputed tomographydeep learningopportunistic screening

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Computed tomography (CT) for body composition analysis is limited by manual segmentation.
  • Sarcopenia, myosteatosis, and visceral adiposity are clinically relevant prognostic conditions.
  • Automated tools are needed for routine clinical implementation.

Purpose of the Study:

  • To develop and validate FocusedON-BC, an automated deep learning tool for body composition analysis.
  • To screen skeletal muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) from CT scans.
  • To enable opportunistic screening across the T12-L5 vertebral range.

Main Methods:

  • Development of FocusedON-BC, a deep learning tool for automated segmentation.
  • Validation on a multicenter cohort of 518 patients with diverse body mass index (BMI).
  • Benchmarking against expert segmentation using Dice coefficient score (DSC) and mean absolute error (MAE).

Main Results:

  • FocusedON-BC achieved expert-level accuracy with high DSC values for SM, VAT, and SAT.
  • Clinical mean absolute error (MAE) remained below 5% for all compartments.
  • Performance was robust and independent of BMI and CT scanner model.

Conclusions:

  • FocusedON-BC offers accurate, vendor-agnostic body composition and muscle quality analysis.
  • The tool's reliability across diverse phenotypes supports its use in routine nutritional screening.
  • Automated CT-based analysis can overcome limitations of manual segmentation.