ArticleJournal of biomedical optics2026
Python-controlled multimodal UV-VIS hyperspectral imaging system.
Article in Journal of biomedical optics, 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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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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Authors and funding
8 authors.
Funding
Abstract
Significance: Traumatic and chronic wound healing is a complex process that often requires careful observation by healthcare professionals. Noninvasive optical technologies such as hyperspectral reflectance imaging and tissue autofluorescence imaging detect changes in optical properties related to physiological parameters such as tissue perfusion. Macroscopic hyperspectral systems with a large field of view to visualize tissue structure and perfusion are well suited for wound care. Aim: Here, a custom-built benchtop hyperspectral and autofluorescence imaging system (HySAF) is optimized to measure tissue optical properties. It operates using a custom Python script to allow precise control over acquisition parameters. Approach: HySAF was built using off-the-shelf components, and Python code was written to control primary devices. HySAF's performance was evaluated by imaging oxygenated and deoxygenated blood samples, scattering induced by nanoparticles, scorpion autofluorescence, and rat wounds. Results: HySAF has a large field of view (FOV) with Conclusion: HySAF is capable of detecting changes in sample fluorescence and absorbance across visible light with large FOV and could be implemented in clinical applications such as monitoring and assessing wound healing.
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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 Socratic graph.