ArticleJournal of biophotonics2024
Deep learning classification of ex vivo human colon tissues using spectroscopic optical coherence tomography.
Article in Journal of biophotonics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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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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
4 citing papers in PubMed.
- Spectroscopic correlation tomography (SpCT) for visualization of spatial correlations in volumetric OCT scans.Biomedical optics express · 2026Article
- Spectroscopic analysis of volumetric OCT data for the automated measurement of scatterer size.Journal of the Optical Society of America. A, Optics, image science, and vision · 2025Article
- Deep learning for the detection of colon polyps with malignant potential: ex vivo classification using feature-enhanced optical coherence tomography (OCT) images.Biomedical optics express · 2025Article
- Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens.Light, science & applications · 2024Article
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11 authors.
Funding
Abstract
Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.
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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.