ArticleScientific reports2017
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 6 of them syntheses that pooled it.
What it found
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Who cites it
94 citing papers in PubMed, 6 syntheses or guidelines pooled it, 322 citations in OpenAlex.
- Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.Oral diseases · 2025Pooled it
- Application and performance of artificial intelligence-based models in the detection, segmentation and classification of periapical lesions: a systematic review.Frontiers in dental medicine · 2025Pooled it
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- In Vivo Imaging-Based Techniques for Early Diagnosis of Oral Potentially Malignant Disorders-Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2021Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Article
- Evaluation of the Detectability of Oral Potentially Malignant Diseases with a Deep Learning Approach: A Retrospective Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.Head and neck pathology · 2026Review
- Deep visual detection system for oral squamous cell carcinoma.Scientific reports · 2026Article
- Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.Oral diseases · 2025Article
- Detect pre-cancerous tongue lesions for early oral cancer diagnosis using deep learning algorithm.Scientific reports · 2025Article
- Machine Learning Based Multi-Class Classification and Grading of Squamous Cell Carcinoma in Optical Microscopy.Microscopy research and technique · 2025Article
- Review
- The Laryngovibrogram as a normalized spatiotemporal representation of vocal fold dynamics.Scientific reports · 2025Article
- Characterization of irradiated mucosa using confocal laser endomicroscopy in the upper aerodigestive tract.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Article
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Applications of Artificial Intelligence in Dental Medicine: A Critical Review.International dental journal · 2025Review
34 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
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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.