ArticleJournal of biophotonics2020
The use of optical coherence tomography and convolutional neural networks to distinguish normal and abnormal oral mucosa.
Article in Journal of biophotonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Role of optical imaging in oral cancer and oral potentially malignant disorders: implications for oral submucous fibrosis.Frontiers in oral health · 2025Review
- Critical review of OCT in clinical practice for the assessment of oral lesions.Frontiers in oncology · 2025Review
- Nanotechnology in the perioperative treatment of head and neck cancer: application and outlook.Frontiers in bioengineering and biotechnology · 2025Review
- Enhancing Oral Cancer Detection: A Systematic Review of the Diagnostic Accuracy and Future Integration of Optical Coherence Tomography with Artificial Intelligence.Journal of clinical medicine · 2024Review
- Illuminating the future of precision cancer surgery with fluorescence imaging and artificial intelligence convergence.NPJ precision oncology · 2024Review
- Prospects of artificial intelligence in dentistry.Dental research journal · 2024Article
- The optimization and application of photodynamic diagnosis and autofluorescence imaging in tumor diagnosis and guided surgery: current status and future prospects.Frontiers in oncology · 2024Review
- Artificial intelligence in dentistry-A review.Frontiers in dental medicine · 2023Review
- Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images.Radiology research and practice · 2022Review
- Application of optical coherence tomography to study the structural features of oral mucosa in biopsy tissues of oral dysplasia and carcinomas.Clinical oral investigations · 2021Article
- Article
- Mesoscopic fluorescence lifetime imaging: Fundamental principles, clinical applications and future directions.Journal of biophotonics · 2021Review
- Advances in Doppler optical coherence tomography and angiography.Translational biophotonics · 2019Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Incomplete surgical resection of head and neck squamous cell carcinoma (HNSCC) is the most common cause of local HNSCC recurrence. Currently, surgeons rely on preoperative imaging, direct visualization, palpation and frozen section to determine the extent of tissue resection. It has been demonstrated that optical coherence tomography (OCT), a minimally invasive, nonionizing near infrared mesoscopic imaging modality can resolve subsurface differences between normal and abnormal head and neck mucosa. Previous work has utilized two-dimensional OCT imaging which is limited to the evaluation of small regions of interest generated frame by frame. OCT technology is capable of performing rapid volumetric imaging, but the capacity and expertise to analyze this massive amount of image data is lacking. In this study, we evaluate the ability of a retrained convolutional neural network to classify three-dimensional OCT images of head and neck mucosa to differentiate normal and abnormal tissues with sensitivity and specificity of 100% and 70%, respectively. This method has the potential to serve as a real-time analytic tool in the assessment of surgical margins.
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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.