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Opt. Express"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>\n                    Osteoporosis is a common skeletal disorder characterized by reduced bone mineral density (BMD) and elevated fracture risk, particularly in aging populations. This study presents a machine learning framework for intelligent optical bone densitometry (iOBD) using near-infrared (NIR) wrist imaging as a non-invasive alternative to dual-energy X-ray absorptiometry (DXA). Among six imaging configurations (three wavelengths \u00d7 bilateral wrists), 940\u2009nm left wrist images were selected based on superior tissue penetration and reduced inter-subject variability. Features extracted from preprocessed NIR images using a pre-trained VGG16 network were combined with physiological data to predict BMD at five DXA sites. Model benchmarking across eight feature\u2013regressor combinations identified the VGG16 + KNN model as optimal, achieving a test mean absolute percentage error (MAPE) of 8.5% and a correlation coefficient of\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.718 at the one-third distal radius. Prediction performance declined at distant sites, with spine (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.460) and femur (\n                    <jats:italic>r<\/jats:italic>\n                    \u2009&lt;\u20090.15) showing limited generalization, particularly in elderly subjects. These results suggest that iOBD can serve as a radiation-free triage tool in primary care, helping identify who should be referred for confirmatory DXA while lowering cost and scan time.\n                  <\/jats:p>","DOI":"10.1364\/boe.575311","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T20:00:08Z","timestamp":1764014408000},"page":"2764","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generalizable framework for multi-site bone density prediction using non-dominant wrist optical biomarkers"],"prefix":"10.1364","volume":"17","author":[{"given":"Wei-Chun","family":"Chang","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00se2k293","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Wanfang Hospital (Managed by Taipei Medical University)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takhellambam Gautam","family":"Meitei","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00se2k293","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Min","family":"Wang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00se2k293","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang-Kung","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00se2k293","id-type":"ROR","asserted-by":"publisher"}]},{"name":"National Yang Ming Chiao Tung University"},{"name":"Taipei Veterans General Hospital"},{"name":"Taipei Municipal Gan Dau Hospital (Managed by Taipei Veterans General Hospital)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9769-8019","authenticated-orcid":true,"given":"Chia-Wei","family":"Sun","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00se2k293","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"key":"boe-17-6-2764-R3","doi-asserted-by":"publisher","first-page":"1759720X","DOI":"10.1177\/1759720X211028360","type":"journal-article","volume":"13","author":"Chen","year":"2021","journal-title":"Ther. 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