ArticleJournal of biomedical optics2025
Deep learning-enabled fluorescence imaging for surgical guidance:
Article in Journal of biomedical optics, 2025. 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Depth-sensitive optical property characterization using multi-frequency laparoscopic spatial frequency domain imaging.Biomedical optics express · 2026Article
- Deep learning-enabled fluorescence imaging for oral cancer margin classification in preclinical models.Journal of biomedical optics · 2025Article
- Photosensitizer spatial heterogeneity and its impact on personalized interstitial photodynamic therapy treatment planning.Journal of biomedical optics · 2025Article
- Speckle-illumination spatial frequency domain imaging with a stereo laparoscope for profile-corrected optical property mapping.Journal of biomedical optics · 2025Article
- Introduction to the Special Issue on Molecular Guided Surgery.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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Significance: Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current Aim: A DL-enabled fluorescence spatial frequency domain imaging (SFDI) system trained with Approach: A convolutional neural network was designed to produce tumor depth and concentration maps from SFDI images. Three Results: The performance of the CSH model was superior when presented with patient-derived tumors ( Conclusions: A DL-enabled SFDI system trained with
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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.