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A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT.

Joseph E Burns1, Jianhua Yao2, Didier Chalhoub2

  • 1Department of Radiological Sciences, University of California-Irvine School of Medicine, Orange, California.

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A new deep learning system accurately analyzes abdominal CT scans to detect truncal musculature for identifying sarcopenia. This automated tool shows high performance in segmenting muscles across lumbar levels.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Geriatric Medicine

Background:

  • Sarcopenia, an age-related loss of muscle mass and strength, is a significant health concern.
  • Accurate assessment of truncal musculature is crucial for sarcopenia detection.
  • Current methods for muscle analysis can be time-consuming and labor-intensive.

Purpose of the Study:

  • To evaluate the accuracy of a fully-automated deep learning system for detecting and analyzing truncal musculature on abdominal CT scans.
  • To assess the system's potential for identifying central sarcopenia.

Main Methods:

  • Development of a computer system for automated segmentation of truncal musculature groups.
  • Utilized abdominal CT scans from 102 patients (mean age 68 years).
  • Manual segmentation of truncal musculature served as the reference standard for training and testing the system, with Dice similarity coefficients used for performance evaluation.

Main Results:

  • The system achieved high Dice similarity coefficients for total abdominal muscle cross-section detection at L3 (0.953 ± 0.015 training, 0.938 ± 0.028 testing) and L4 (0.953 ± 0.011 training, 0.940 ± 0.026 testing).
  • Excellent performance was also observed for total psoas muscle cross-section detection at L3 (0.942 ± 0.040 training, 0.939 ± 0.028 testing) and L4 (0.951 ± 0.037 training, 0.946 ± 0.032 testing).

Conclusions:

  • The fully-automated deep learning system accurately segments multiple muscle groups at all lumbar spine levels on abdominal CT.
  • This system demonstrates significant potential for the automated detection of sarcopenia.