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Related Experiment Video

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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

Matthew H Lee1, Daniel Liu2, John W Garrett2

  • 1Department of Radiology, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI, 53792, USA. mlee5@uwhealth.org.

Abdominal Radiology (New York)
|December 29, 2023
PubMed
Summary

Automated body composition analysis using artificial intelligence (AI) on abdominal CT scans shows high correlation between thin and thick slices. This indicates that both datasets are feasible for AI-driven body composition algorithms, especially for soft tissue measures.

Keywords:
AIAbdominal CTBody compositionThick sliceThin slice

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Body composition analysis provides valuable insights into patient health and disease prognosis.
  • Automated analysis of CT scans offers a scalable approach to body composition assessment.

Purpose of the Study:

  • To compare fully automated artificial intelligence (AI)-derived body composition measures from thin (1.25 mm) and thick (5 mm) slice abdominal CT data.
  • To evaluate the feasibility of using both thin and thick slice CT data for AI-based body composition algorithms.

Main Methods:

  • A retrospective study analyzed 9882 abdominal CT scans (unenhanced and contrast-enhanced).
  • Fully automated AI algorithms quantified bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR), and aortic calcium.
  • Measures from thin and thick slices were compared using correlation coefficients and Bland-Altman analysis.

Main Results:

  • Very strong positive correlations (r² > 0.92) were observed for all soft tissue measures and VSR.
  • Moderate correlation was found for bone attenuation (r² = 0.35).
  • Bland-Altman analysis demonstrated strong agreement for most soft tissue measures, with mean percentage differences < 5% for VSR, muscle area, liver attenuation, and liver volume.

Conclusions:

  • Automated body composition measures derived from thin and thick abdominal CT slices are strongly correlated and show agreement.
  • The findings suggest feasibility in using either thin or thick slice CT data for these AI-based body composition algorithms, particularly for soft tissue applications.