ArticleJournal of biomedical optics2024
Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering.
Article in Journal of biomedical optics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Geometric decoupling of epidermal melanin and dermal hemoglobin signals using coaxial and spatially resolved NIRS.Journal of biomedical optics · 2026Article
- Functional near-infrared spectroscopy: Systematic mapping of abnormal brain function features in neurological disorders.Neural regeneration research · 2026Article
- Emerging non-invasive tools for hair follicle assessment: a narrative review on hyperspectral and terahertz imaging.Lasers in medical science · 2026Review
- Hyperspectral image wavelength band and texture characteristics related to elasticity values for each body part for non-invasive skin elasticity analysis.Biomedical optics express · 2025Article
- Special Section Guest Editorial: JBO Special Section on Hyperspectral Imaging.Journal of biomedical optics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time. Aim: A hyperspectral camera was used to capture Approach: The optical properties of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering were used with multi-layer Monte Carlo models to generate simulated diffuse reflectance spectra for 24,000 random combinations of physiologically relevant tissue components. These spectra were then used to train an artificial neural network (ANN) to predict tissue component concentrations from an input reflectance spectrum. Results: The ANN achieved low root mean square errors in a test set of 6000 independent simulated diffuse reflectance spectra while calculating concentration values more than 4000× faster than a conventional iterative least squares approach. Conclusions:
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Registered trials
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