ODIASP: An Open User-Friendly Software for Automated SMI Determination—Application to an Inpatient Population

Abstract

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.