ArticleJournal of clinical medicine2021
Deep Learning on Oral Squamous Cell Carcinoma Ex Vivo Fluorescent Confocal Microscopy Data: A Feasibility Study.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 45 citations in OpenAlex.
- Deep visual detection system for oral squamous cell carcinoma.Scientific reports · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification.Diagnostics (Basel, Switzerland) · 2025Article
- gamUnet: designing global attention-based CNN architectures for enhanced oral cancer detection and segmentation.Frontiers in medicine · 2025Article
- Exploring the efficacy of various CNN architectures in diagnosing oral cancer from squamous cell carcinoma.MethodsX · 2024Article
- A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs.Biomimetics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks.European journal of dentistry · 2023Article
- Potential Use of Vivascope for Real-Time Histological Evaluation in Endoscopic Laryngeal Surgery.Journal of personalized medicine · 2023Article
- Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists.Scientific reports · 2023Article
- Current Approach and Future Directions in the Diagnosis and Prognosis of Keratinocyte Carcinomas.Journal of clinical medicine · 2023Article
- Article
- Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Oral squamous cell carcinoma detection using EfficientNet on histopathological images.Frontiers in medicine · 2023Article
- Recent Advances in Oral Squamous Cell Carcinoma.Journal of clinical medicine · 2022Article
- Deep Learning on Basal Cell Carcinoma In Vivo Reflectance Confocal Microscopy Data.Journal of personalized medicine · 2022Article
- The Value of In Vivo Reflectance Confocal Microscopy as an Assessment Tool in Chemotherapy-Induced Peripheral Neuropathy: A Pilot Study.The oncologist · 2022Article
- Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial Intelligence-Based Prediction of Oroantral Communication after Tooth Extraction Utilizing Preoperative Panoramic Radiography.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEx vivo fluorescent confocal microscopy (FCM) is a novel and effective method for a fast-automatized histological tissue examination. In contrast, conventional diagnostic methods are primarily based on the skills of the histopathologist. In this study, we investigated the potential of convolutional neural networks (CNNs) for automatized classification of oral squamous cell carcinoma via ex vivo FCM imaging for the first time. MATERIAL AND
methodsTissue samples from 20 patients were collected, scanned with an ex vivo confocal microscope immediately after resection, and investigated histopathologically. A CNN architecture (MobileNet) was trained and tested for accuracy.
resultsThe model achieved a sensitivity of 0.47 and specificity of 0.96 in the automated classification of cancerous tissue in our study.
conclusionIn this preliminary work, we trained a CNN model on a limited number of ex vivo FCM images and obtained promising results in the automated classification of cancerous tissue. Further studies using large sample sizes are warranted to introduce this technology into clinics.
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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.