ODIASP: An Open‐Source 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 leve...

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Vydané v:	Journal of cachexia, sarcopenia and muscle Ročník 16; číslo 4; s. e70023 - n/a
Hlavní autori:	Charrière, Katia, Ragusa, Antoine, Genoux, Béatrice, Vilotitch, Antoine, Artemova, Svetlana, Dumont, Charlène, Beaudoin, Paul‐Antoine, Madiot, Pierre‐Ephrem, Ferretti, Gilbert R., Bricault, Ivan, Fontaine, Eric, Bosson, Jean‐Luc, Moreau‐Gaudry, Alexandre, Giai, Joris, Bétry, Cécile
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Germany John Wiley & Sons, Inc 01.08.2025 Wiley
Predmet:	Adult Aged Algorithms Automation body composition Clinical medicine computational neural networks computer‐assisted Ethics Female Hospitals Humans image processing Inpatients Male Malnutrition Medical imaging Middle Aged Muscle, Skeletal - diagnostic imaging Musculoskeletal system Retrospective Studies sarcopenia Sarcopenia - diagnosis Sarcopenia - diagnostic imaging skeletal muscle Software Tomography, X-Ray Computed Values Vertebrae image processing malnutrition body composition computer‐assisted skeletal muscle computational neural networks sarcopenia
ISSN:	2190-5991, 2190-6009, 2190-6009
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Abstract	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 is aimed at developing and validating an open‐access, simple 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 centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross‐sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed. Results SMI was available for 2503 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). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825–0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982–0.986) after correcting for systematic overestimation (a 5.8 cm2 [5.4–6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings. Conclusions This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open‐source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment.
AbstractList	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 is aimed at developing and validating an open-access, simple software tool called ODIASP for automated SMI determination. 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 centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed. SMI was available for 2503 participants, 53.3% male, with a median age of 66 years (51-78) and a median BMI of 24.8 kg/m (21.7-28.7). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825-0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982-0.986) after correcting for systematic overestimation (a 5.8 cm [5.4-6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings. This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open-source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment. 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 is aimed at developing and validating an open‐access, simple 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 centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross‐sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed. Results SMI was available for 2503 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). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825–0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982–0.986) after correcting for systematic overestimation (a 5.8 cm2 [5.4–6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings. Conclusions This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open‐source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment. 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 is aimed at developing and validating an open‐access, simple 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 centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross‐sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed. Results SMI was available for 2503 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). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825–0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982–0.986) after correcting for systematic overestimation (a 5.8 cm2 [5.4–6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings. Conclusions This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open‐source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment. 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 is aimed at developing and validating an open-access, simple software tool called ODIASP for automated SMI determination.BACKGROUNDThe 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 is aimed at developing and validating an open-access, simple software tool called ODIASP for automated SMI determination.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 centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed.METHODSData 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 centre who underwent at least one CT scan capturing images at the L3 vertebral level and had a recorded height were included. ODIASP combines two algorithms to automate L3 slice selection and skeletal muscle segmentation, ensuring a seamless process. Agreement between cross-sectional muscle area (CSMA) values obtained using ODIASP and the reference methodology (i.e., manual determination) was evaluated using the intraclass correlation coefficient (ICC). The prevalence of reduced SMI was also assessed.SMI was available for 2503 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). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825-0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982-0.986) after correcting for systematic overestimation (a 5.8 cm2 [5.4-6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings.RESULTSSMI was available for 2503 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). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825-0.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982-0.986) after correcting for systematic overestimation (a 5.8 cm2 [5.4-6.3]) indicating excellent agreement. The prevalence of reduced SMI was 9.1% overall (11.0% in men and 6.6% in women). The ODIASP software is available as a downloadable executable to support its use in research settings.This study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open-source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment.CONCLUSIONSThis study demonstrates that ODIASP is a reliable tool for automated SMI at the L3 vertebra level from CT scans. The integration of validated AI algorithms into a simple, open-source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment.
Author	Charrière, Katia Genoux, Béatrice Artemova, Svetlana Beaudoin, Paul‐Antoine Bricault, Ivan Fontaine, Eric Ragusa, Antoine Moreau‐Gaudry, Alexandre Vilotitch, Antoine Giai, Joris Ferretti, Gilbert R. Dumont, Charlène Madiot, Pierre‐Ephrem Bosson, Jean‐Luc Bétry, Cécile
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Copyright	2025 The Author(s). published by Wiley Periodicals LLC. 2025 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC. 2025. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Keywords	image processing malnutrition body composition computer‐assisted skeletal muscle computational neural networks sarcopenia
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Notes	Funding 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). The funding bodies did not have any involvement in the design/conduct of the research, in data analysis/interpretation or in writing/approval of the manuscript. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
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Snippet	ABSTRACT Background The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including... The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including muscle mass measurement.... ABSTRACT Background The diagnosis of malnutrition has evolved with the GLIM recommendations, which advocate for integrating phenotypic criteria, including...
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SubjectTerms	Adult Aged Algorithms Automation body composition Clinical medicine computational neural networks computer‐assisted Ethics Female Hospitals Humans image processing Inpatients Male Malnutrition Medical imaging Middle Aged Muscle, Skeletal - diagnostic imaging Musculoskeletal system Retrospective Studies sarcopenia Sarcopenia - diagnosis Sarcopenia - diagnostic imaging skeletal muscle Software Tomography, X-Ray Computed Values Vertebrae
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Title	ODIASP: An Open‐Source Software for Automated SMI Determination—Application to an Inpatient Population
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