Background: The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including muscle mass measurement. The GLIM framework specifically suggests using skeletal muscle index (SMI) assessed via CT scan at the third lumbar level (L3) as a first-line approach. However, manual segmentation of muscle from CT images is often time-consuming and infrequently performed in clinical practice. This study aims to develop and validate an open-access, user-friendly software tool called ODIASP for automated SMI determination. Methods: Data were retrospectively collected from a clinical data warehouse at Grenoble Alpes University Hospital, including epidemiological and imaging data from CT scans. All consecutive adult patients admitted in 2018 to our tertiary center who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. The ODIASP tool combines two algorithms to automatically perform L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained via ODIASP and reference methodology was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed. Results: SMI values were available for 2,503 participants, 53.3% male, with a median age of 66 years [51-78] and a median BMI of 24.8 kg/m2 [21.7-28.7]. There was substantial agreement between the reference method and ODIASP (ICC: 0.971; 95% CI: 0.825 to 0.989) in a validation subset of 674 CT scans. After correcting for systematic errors (a 5.8 cm2 [5.4-6.3] overestimation of the CSMA), the agreement improved to 0.984 (95% CI: 0.982 to 0.986), indicating excellent agreement. The prevalence of reduced SMI was estimated at 9.1% overall (11.0% in men and 6.6% in women). To facilitate usage, the ODIASP software is encapsulated in a user-friendly interface. Conclusions: This study demonstrates that ODIASP is a reliable tool for automated muscle segmentation at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a user-friendly platform enhances the ability to assess SMI in diverse patient cohorts, ultimately contributing to improved patient outcomes through more accurate assessments of malnutrition and sarcopenia.

The authors have declared no competing interest.

This work was supported by a grant from the Regional Delegation for Clinical Research of the University Hospital Grenoble Alpes in 2019 and MIAI@Grenoble Alpes, (ANR-19-P3IA-0003).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethical approval was obtained on 18 August 2021 by the regional ethics committee (CECIC Rhone-Alpes-Auvergne, Clermont-Ferrand, IRB 5891)

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data produced in the present study are available upon reasonable request to the authors (with the exception of the CT scan data, which may be considered identifying)