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ODIASP: An Open-Source Software for Automated SMI Determination-Application to an Inpatient Population.

Katia Charrière1,2, Antoine Ragusa3, Béatrice Genoux1,2

  • 1Public Health Department, Univ. Grenoble Alpes, Clinical Investigation Center-Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, France.

Journal of Cachexia, Sarcopenia and Muscle
|July 27, 2025
PubMed
Summary
This summary is machine-generated.

A new software tool, ODIASP, automates skeletal muscle index (SMI) measurement from CT scans, offering a reliable and efficient method for diagnosing malnutrition according to GLIM criteria. This tool aids in clinical workflows for improved nutritional assessment.

Keywords:
body compositioncomputational neural networkscomputer‐assistedimage processingmalnutritionsarcopeniaskeletal muscle

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Area of Science:

  • Radiology
  • Medical Imaging
  • Nutritional Science

Background:

  • The Global Leadership Initiative on Malnutrition (GLIM) criteria recommend muscle mass assessment for malnutrition diagnosis.
  • Skeletal Muscle Index (SMI) at the L3 vertebral level via CT scan is a key metric.
  • Manual muscle segmentation from CT scans is time-consuming and limits clinical application.

Purpose of the Study:

  • To develop and validate ODIASP, an open-access software for automated SMI determination from CT scans.
  • To provide a simple, efficient tool for clinical use in nutritional assessment.

Main Methods:

  • Retrospective data collection from a clinical data warehouse (Grenoble Alpes University Hospital).
  • Inclusion of adult patients with CT scans at L3 level and recorded height.
  • Development of ODIASP with algorithms for automated L3 slice selection and skeletal muscle segmentation.
  • Validation using intraclass correlation coefficient (ICC) to compare ODIASP with manual segmentation.

Main Results:

  • SMI data from 2503 participants (53.3% male, median age 66 years).
  • Substantial agreement (ICC: 0.971) and excellent agreement (ICC: 0.984) between ODIASP and manual methods after correction.
  • Prevalence of reduced SMI was 9.1% (11.0% in men, 6.6% in women).

Conclusions:

  • ODIASP is a reliable tool for automated SMI assessment at the L3 level from CT scans.
  • The open-source software enables scalable and standardized SMI assessment.
  • ODIASP supports integration into clinical workflows for enhanced nutritional assessment.