Generalizable framework for multi-site bone density prediction using non-dominant wrist optical biomarkers.

Wei-Chun Chang, Takhellambam Gautam Meitei, Yi-Min Wang, Liang-Kung Chen, Chia-Wei Sun

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Article in Biomedical optics express, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

5 authors.

Wei-Chun ChangBiomedical Optical Intelligent Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Takhellambam Gautam MeiteiBiomedical Optical Intelligent Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Yi-Min WangBiomedical Optical Intelligent Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.

Liang-Kung ChenCenter for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan.

Chia-Wei SunBiomedical Optical Intelligent Lab, Department of Photonics, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.ORCID https://orcid.org/0000-0001-9769-8019

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 × bilateral wrists), 940 nm 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-regressor combinations identified the VGG16 + KNN model as optimal, achieving a test mean absolute percentage error (MAPE) of 8.5% and a correlation coefficient of

Identifiers

PMID42311317

DOI10.1364/BOE.575311

PMCPMC13271249

What OpenQuestion holds

Textmetadata

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.