ArticleJournal of biomedical optics2022
Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed, 2 citations in OpenAlex.
- Multidirectional Optical Bone Densitometry Using a Simulation-Based Machine Learning Model: Experimental Validation with Bone Phantoms.Annals of biomedical engineering · 2026Article
- Light transport in the human spine for optical bone density assessment: Monte Carlo modeling and validation.Biomedical optics express · 2025Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
significanceTo achieve early detection of osteoporosis, a simple bone densitometry method using optics was proposed. However, individual differences in soft tissue structure and optical properties can cause errors in quantitative bone densitometry. Therefore, developing optical bone densitometry that is robust to soft tissue variations is important for the early detection of osteoporosis.
aimThe purpose of this study was to develop an optical bone densitometer that is insensitive to soft tissue, using Monte Carlo simulation and machine learning techniques, and to verify its feasibility. APPROACH: We propose a method to measure spatially resolved diffuse light from three directions of the biological tissue model and used machine learning techniques to predict bone density from these data. The three directions are backward, forward, and lateral to the direction of ballistic light irradiation. The method was validated using Monte Carlo simulations using synthetic biological tissue models with 1211 different random structural and optical properties.
resultsThe results were computed after a 10-fold cross-validation. From the simulated optical data, the machine learning model predicted bone density with a coefficient of determination of 0.760.
conclusionsThe optical bone densitometry method proposed in this study was found to be robust against individual differences in soft tissue.
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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.