{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:48:33Z","timestamp":1786978113150,"version":"build-2736575974"},"reference-count":33,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T00:00:00Z","timestamp":1753488000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J cachexia sarcopenia muscle"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u2010line approach. However, manual segmentation of muscle from CT images is often time\u2010consuming and infrequently performed in clinical practice. This study is aimed at developing and validating an open\u2010access, simple software tool called ODIASP for automated SMI determination.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2010sectional 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      SMI was available for 2503 participants, 53.3% male, with a median age of 66\u2009years (51\u201378) and a median BMI of 24.8\u2009kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      (21.7\u201328.7). In a validation subset of 674 scans, agreement between the reference method and ODIASP was substantial (ICC: 0.971; 95% CI: 0.825\u20130.989) and improved to excellent (ICC: 0.984; 95% CI: 0.982\u20130.986) after correcting for systematic overestimation (a 5.8\u2009cm\n                      <jats:sup>2<\/jats:sup>\n                      [5.4\u20136.3]) 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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\u2010source software enables scalable, standardised assessment of SMI in diverse patient populations and supports future integration into clinical workflows for improved nutritional assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jcsm.70023","type":"journal-article","created":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T11:43:35Z","timestamp":1753530215000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["ODIASP: An Open\u2010Source Software for Automated SMI Determination\u2014Application to an Inpatient Population"],"prefix":"10.1002","volume":"16","author":[{"given":"Katia","family":"Charri\u00e8re","sequence":"first","affiliation":[{"name":"Public Health Department Univ. Grenoble Alpes, Clinical Investigation Center\u2010Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes  Grenoble France"},{"name":"Univ. Grenoble Alpes, M\u00e9thodologie de l'information en Sant\u00e9, Biostatistiques, Recherche clinique et Innovation Technologique, P\u00f4le Sant\u00e9 Publique, CHU Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Ragusa","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B\u00e9atrice","family":"Genoux","sequence":"additional","affiliation":[{"name":"Public Health Department Univ. Grenoble Alpes, Clinical Investigation Center\u2010Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes  Grenoble France"},{"name":"Univ. Grenoble Alpes, M\u00e9thodologie de l'information en Sant\u00e9, Biostatistiques, Recherche clinique et Innovation Technologique, P\u00f4le Sant\u00e9 Publique, CHU Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antoine","family":"Vilotitch","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, M\u00e9thodologie de l'information en Sant\u00e9, Biostatistiques, Recherche clinique et Innovation Technologique, P\u00f4le Sant\u00e9 Publique, Unit\u00e9 d'ing\u00e9nierie des donn\u00e9es, P\u00f4le Sant\u00e9 Publique, Grenoble Alpes University Hospital  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Artemova","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, M\u00e9thodologie de l'information en Sant\u00e9, Biostatistiques, Recherche clinique et Innovation Technologique, P\u00f4le Sant\u00e9 Publique, CHU Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charl\u00e8ne","family":"Dumont","sequence":"additional","affiliation":[{"name":"Public Health Department Univ. Grenoble Alpes, Clinical Investigation Center\u2010Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes  Grenoble France"},{"name":"Univ. Grenoble Alpes, M\u00e9thodologie de l'information en Sant\u00e9, Biostatistiques, Recherche clinique et Innovation Technologique, P\u00f4le Sant\u00e9 Publique, CHU Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul\u2010Antoine","family":"Beaudoin","sequence":"additional","affiliation":[{"name":"Public Health Department Univ. Grenoble Alpes, Clinical Investigation Center\u2010Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes  Grenoble France"},{"name":"Univ. Grenoble Alpes, M\u00e9thodologie de l'information en Sant\u00e9, Biostatistiques, Recherche clinique et Innovation Technologique, P\u00f4le Sant\u00e9 Publique, CHU Grenoble Alpes  Grenoble France"},{"name":"Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, TIMC  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre\u2010Ephrem","family":"Madiot","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, Direction des Services Num\u00e9riques, Grenoble Alpes University Hospital  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilbert\u00a0R.","family":"Ferretti","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, INSERM U1209, IAB, CHU Grenoble Alpes, Service de radiologie diagnostique et interventionnelle  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Bricault","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, INSERM U1209, IAB, CHU Grenoble Alpes, Service de radiologie diagnostique et interventionnelle  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Fontaine","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, Diabetology and Nutrition Univ. Grenoble Alpes, INSERM U1055, LBFA, CHU Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean\u2010Luc","family":"Bosson","sequence":"additional","affiliation":[{"name":"Public Health Department Univ. Grenoble Alpes, Clinical Investigation Center\u2010Technological Innovation, INSERM CIC1406, CHU Grenoble Alpes  Grenoble France"},{"name":"Univ. 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Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnu.2018.08.002"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nupar.2023.05.005"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnu.2022.02.001"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnesp.2020.02.001"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nut.2021.111227"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnume.2023.1292676"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2022.01.036"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-00161-5"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16261"},{"key":"e_1_2_9_11_1","unstructured":"S.Artemova P\u2010E.Madiot A.Caporossi P. 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