{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:45:21Z","timestamp":1785545121193,"version":"3.56.0"},"reference-count":47,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Aging"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with dementia, are still lacking.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study\u2019s main objective was to assess the feasibility of using deep learning techniques for segmentation and quantification of musculoskeletal tissues in magnetic resonance imaging (MRI) scans of the head in patients with neurocognitive disorders. This study aimed to pave the way for using automated techniques for opportunistic detection of sarcopenia in patients with neurocognitive disorder.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In a cross-sectional analysis of 53 participants, we used 7 U-Net-like deep learning models to segment 5 different tissues in head MRI images and used the Dice similarity coefficient and average symmetric surface distance as main assessment techniques to compare results. We also analyzed the relationship between BMI and muscle and fat volumes.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Our framework accurately quantified masseter and subcutaneous fat on the left and right sides of the head and tongue muscle (mean Dice similarity coefficient 92.4%). A significant correlation exists between the area and volume of tongue muscle, left masseter muscle, and BMI.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Our study demonstrates the successful application of a deep learning model to quantify muscle volumes in head MRI in patients with neurocognitive disorders. This is a promising first step toward clinically applicable artificial intelligence and deep learning methods for estimating masseter and tongue muscle and predicting adverse outcomes in this population.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/63686","type":"journal-article","created":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T09:56:56Z","timestamp":1735811816000},"page":"e63686-e63686","source":"Crossref","is-referenced-by-count":2,"title":["Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study"],"prefix":"10.2196","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1374-5709","authenticated-orcid":false,"given":"Mahdi","family":"Imani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5832-0603","authenticated-orcid":false,"given":"Miguel G","family":"Borda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1597-9421","authenticated-orcid":false,"given":"Sara","family":"Vogrin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8015-8358","authenticated-orcid":false,"given":"Erik","family":"Meijering","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6314-216X","authenticated-orcid":false,"given":"Dag","family":"Aarsland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8126-0637","authenticated-orcid":false,"given":"Gustavo","family":"Duque","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"key":"R1","unstructured":"WHO G . 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