ArticleInternational journal of environmental research and public health2023
Use of Artificial Intelligence in the Classification of Elementary Oral Lesions from Clinical Images.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Awareness, usage, and perspectives on ChatGPT in dental education among graduate and undergraduate students.Scientific reports · 2025Article
- Vision-based diagnostic gain of ChatGPT-5 and gemini 2.5 pro compared with human experts in oral lesion assessment.Scientific reports · 2025Article
- Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.Oral diseases · 2025Article
- Perception of Medical Undergraduates on Artificial Intelligence in Medical Education: Qualitative Exploration.JMIR medical education · 2025Article
- Differentiation of benign and malignant oral lesions through surface texture analysis and SVM modeling.Clinical oral investigations · 2025Article
- Neural network models for diagnosing recurrent aphthous ulcerations from clinical oral images.Scientific reports · 2025Article
- Performance of AI Chatbots in Preliminary Diagnosis of Maxillofacial Pathologies.Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Application of image enhancement in the auxiliary diagnosis of oral potentially malignant disorders.Clinical oral investigations · 2025Article
- Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images.Scientific reports · 2025Article
- Current AI Applications and Challenges in Oral Pathology.Oral (Basel, Switzerland) · 2025Article
- The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review.Oral and maxillofacial surgery · 2025Review
- Recent advance in early oral lesion diagnosis: the application of artificial intelligence-assisted endoscopy.Frontiers in oncology · 2025Review
- Attention-guided convolutional network for bias-mitigated and interpretable oral lesion classification.Scientific reports · 2024Article
- Deep Convolutional Neural Network for Accurate Classification of Myofibroblastic Lesions on Patch-Based Images.Head and neck pathology · 2024Article
- Article
- Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine.BMC medical education · 2024Article
- Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network.BMC medical imaging · 2024Article
- Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesArtificial intelligence has generated a significant impact in the health field. The aim of this study was to perform the training and validation of a convolutional neural network (CNN)-based model to automatically classify six clinical representation categories of oral lesion images.
methodThe CNN model was developed with the objective of automatically classifying the images into six categories of elementary lesions: (1) papule/nodule; (2) macule/spot; (3) vesicle/bullous; (4) erosion; (5) ulcer and (6) plaque. We selected four architectures and using our dataset we decided to test the following architectures: ResNet-50, VGG16, InceptionV3 and Xception. We used the confusion matrix as the main metric for the CNN evaluation and discussion.
resultsA total of 5069 images of oral mucosa lesions were used. The oral elementary lesions classification reached the best result using an architecture based on InceptionV3. After hyperparameter optimization, we reached more than 71% correct predictions in all six lesion classes. The classification achieved an average accuracy of 95.09% in our dataset.
conclusionsWe reported the development of an artificial intelligence model for the automated classification of elementary lesions from oral clinical images, achieving satisfactory performance. Future directions include the study of including trained layers to establish patterns of characteristics that determine benign, potentially malignant and malignant lesions.
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