ArticleBMC oral health2024
Oral epithelial dysplasia detection and grading in oral leukoplakia using deep learning.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Artificial Intelligence-Assisted Histopathologic Diagnosis and Grading of Oral Epithelial Dysplasia: A Systematic Review and Functional Meta-synthesis.Head and neck pathology · 2026Pooled it
- CD1a Expression in Oral Dysplastic Lesions: An Immunohistochemical Analysis.Journal of dentistry (Shiraz, Iran) · 2026Article
- Efficacy of 5-ALA Photodynamic Therapy in Dysplastic Oral Leukoplakia: Systematic Review and Meta-Analysis.Pharmaceutics · 2026Review
- Salivary Oxidative Stress and Antioxidant Markers in Oral Leukoplakia: A Systematic Review and Meta-Analysis.Antioxidants (Basel, Switzerland) · 2026Review
- Laser Therapy Versus Conventional Treatments in Oral Leukoplakia: A Systematic Review of Efficacy, Recurrence, and Postoperative Outcomes.International journal of dentistry · 2026Review
- Prospective applications of artificial intelligence for the diagnosis of oral leukoplakia: a scoping review.Frontiers in oral health · 2026Review
- Assessment of Deep Convolutional Neural Network Models for the Classification of Benign Fibro-Osseous Lesions of the Jaws.Clinical and experimental dental research · 2025Article
- AI-driven prediction of progression to oral squamous cell carcinoma using a multiresolution pathology model.NPJ digital medicine · 2025Article
- Next-generation AI framework for comprehensive oral leukoplakia evaluation and management.NPJ digital medicine · 2025Article
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- Oral squamous cell carcinoma grading classification using deep transformer encoder assisted dilated convolution with global attention.Frontiers in artificial intelligence · 2025Article
- Head and neck cancer care: advanced molecular and genetic diagnostics.EXCLI journal · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundThe grading of oral epithelial dysplasia is often time-consuming for oral pathologists and the results are poorly reproducible between observers. In this study, we aimed to establish an objective, accurate and useful detection and grading system for oral epithelial dysplasia in the whole-slides of oral leukoplakia.
methodsFour convolutional neural networks were compared using the image patches from 56 whole-slide of oral leukoplakia labeled by pathologists as the gold standard. Sequentially, feature detection models were trained, validated and tested with 1,000 image patches using the optimal network. Lastly, a comprehensive system named E-MOD-plus was established by combining feature detection models and a multiclass logistic model.
resultsEfficientNet-B0 was selected as the optimal network to build feature detection models. In the internal dataset of whole-slide images, the prediction accuracy of E-MOD-plus was 81.3% (95% confidence interval: 71.4-90.5%) and the area under the receiver operating characteristic curve was 0.793 (95% confidence interval: 0.650 to 0.925); in the external dataset of 229 tissue microarray images, the prediction accuracy was 86.5% (95% confidence interval: 82.4-90.0%) and the area under the receiver operating characteristic curve was 0.669 (95% confidence interval: 0.496 to 0.843).
conclusionsE-MOD-plus was objective and accurate in the detection of pathological features as well as the grading of oral epithelial dysplasia, and had potential to assist pathologists in clinical practice.
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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.