ArticleAnnals of biomedical engineering2024
Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data.
Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Opportunistic Osteoporosis Screening from Routine Knee Radiographs Using a Deep Learning-Based Texture-Branch Cross-Attention Network.Journal of imaging informatics in medicine · 2026Article
- Multidirectional Optical Bone Densitometry Using a Simulation-Based Machine Learning Model: Experimental Validation with Bone Phantoms.Annals of biomedical engineering · 2026Article
- Machine Learning-Based Automatic Diagnosis of Osteoporosis Using Bone Mineral Density Measurements.Journal of clinical medicine · 2026Article
- Article
- Light transport in the human spine for optical bone density assessment: Monte Carlo modeling and validation.Biomedical optics express · 2025Article
- Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS).Endocrinology, diabetes & metabolism · 2025Article
- The critical role of toll-like receptor 4 in bone remodeling of osteoporosis: from inflammation recognition to immunity.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical bone densitometry (OBD) has been developed for the early detection of osteoporosis. In recent years, machine learning (ML) techniques have been actively implemented for the areas of medical diagnosis and screening with the goal of improving diagnostic accuracy. The purpose of this study was to verify the feasibility of using the combination of OBD and ML techniques as a screening tool for osteoporosis. Dual energy X-ray absorptiometry (DXA) and OBD measurements were performed on 203 Thai subjects. From the OBD measurements and readily available demographic data, machine learning techniques were used to predict the T-score measured by the DXA. The T-score predicted using the Ridge regressor had a correlation of r = 0.512 with respect to the reference value. The predicted T-score also showed an AUC of 0.853 for discriminating individuals with osteoporosis. The results obtained suggest that the developed model is reliable enough to be used for screening for osteoporosis.
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Registered trials
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.