{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T13:02:00Z","timestamp":1792242120632,"version":"4.4.0"},"reference-count":22,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Opportunistic computed tomography (CT) screening for the evaluation of sarcopenia and myosteatosis has been gaining emphasis. A fully automated artificial intelligence (AI)\u2013integrated system for body composition assessment on CT scans is a prerequisite for effective opportunistic screening. However, no study has evaluated the implementation of fully automated AI systems for opportunistic screening in real-world clinical practice for routine health check-ups.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The aim of this study is to evaluate the performance and clinical utility of a fully automated AI-integrated system for body composition assessment on opportunistic CT during routine health check-ups.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This prospective multicenter study included 537 patients who underwent routine health check-ups across 3 institutions. Our AI algorithm models are composed of selecting L3 slice and segmenting muscle and fat area in an end-to-end manner. The AI models were integrated into the Picture Archiving and Communication System (PACS) at each institution. Technical success rate, processing time, and segmentation accuracy in Dice similarity coefficient were assessed. Body composition metrics were analyzed across age and sex groups.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The fully automated AI-integrated system successfully retrieved anonymized CT images from the PACS, performed L3 selection and segmentation, and provided body composition metrics, including muscle quality maps and muscle age. The technical success rate was 100% without any failed cases requiring manual adjustment. The mean processing time from CT acquisition to report generation was 4.12 seconds. Segmentation accuracy comparing AI results and human expert results was 97.4%. Significant age-related declines in skeletal muscle area and normal-attenuation muscle area were observed, alongside increases in low-attenuation muscle area and intramuscular adipose tissue.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Implementation of the fully automated AI-integrated system significantly enhanced opportunistic sarcopenia screening, achieving excellent technical success and high segmentation accuracy without manual intervention. This system has the potential to transform routine health check-ups by providing rapid and accurate assessments of body composition.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/69940","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:30:08Z","timestamp":1748824208000},"page":"e69940-e69940","source":"Crossref","is-referenced-by-count":21,"title":["Implementation of Fully Automated AI-Integrated System for Body Composition Assessment on Computed Tomography for Opportunistic Sarcopenia Screening: Multicenter Prospective Study"],"prefix":"10.2196","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5660-167X","authenticated-orcid":false,"given":"Bushra","family":"Urooj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2181-9555","authenticated-orcid":false,"given":"Yousun","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9742-5351","authenticated-orcid":false,"given":"Seongwon","family":"Na","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5800-3536","authenticated-orcid":false,"given":"In-One","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6690-5357","authenticated-orcid":false,"given":"Eun-Hee","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6432-5897","authenticated-orcid":false,"given":"Seon","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1031-1032","authenticated-orcid":false,"given":"Heeryeol","family":"Jeong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5666-8216","authenticated-orcid":false,"given":"Seungwoo","family":"Khang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4151-6922","authenticated-orcid":false,"given":"Jeongjin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1532-5970","authenticated-orcid":false,"given":"Kyung Won","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"issue":"3","key":"R1","doi-asserted-by":"publisher","first-page":"388","DOI":"10.5230\/jgc.2023.23.e30","article-title":"Artificial intelligence in gastric cancer imaging with emphasis on diagnostic imaging and body morphometry","volume":"23","author":"Kim","journal-title":"J Gastric Cancer"},{"issue":"6","key":"R2","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1093\/ageing\/afu115","article-title":"Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. 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