ArticlePloS one2022
AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 5 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.Clinical oral investigations · 2026Pooled it
- Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review.Oral diseases · 2025Pooled it
- 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
- Trajectory-aware risk stratification of oral lichen planus using a multimodal large language model: a longitudinal diagnostic accuracy study.Scientific reports · 2026Article
- Article
- SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.Oral diseases · 2026Article
- Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.Head and neck pathology · 2026Review
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- A Community-Based Usability Study of an AI-Enabled Oral Cancer Screening App Operated by Village Health Volunteers: Mixed Methods Study.JMIR mHealth and uHealth · 2026Article
- Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- 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
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- Multimodal large language models for oral lesion diagnosis: a systematic review of diagnostic performance and clinical utility.Frontiers in oral health · 2026Review
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- A comparative study of single-stage and dual-stage classification models for OPMDs.BMC oral health · 2025Article
- Artificial intelligence in the study of oral lichen planus characteristics: a review.BDJ open · 2025Review
- Diagnostic Performance of Multimodal Large Language Models in the Analysis of Oral Pathology.Oral diseases · 2025Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classification and detection of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) in oral photographic images. A dataset comprising 980 oral photographic images was divided into 365 images of OSCC, 315 images of OPMDs and 300 images of non-pathological images. Multiclass image classification models were created by using DenseNet-169, ResNet-101, SqueezeNet and Swin-S. Multiclass object detection models were fabricated by using faster R-CNN, YOLOv5, RetinaNet and CenterNet2. The AUC of multiclass image classification of the best CNN models, DenseNet-196, was 1.00 and 0.98 on OSCC and OPMDs, respectively. The AUC of the best multiclass CNN-base object detection models, Faster R-CNN, was 0.88 and 0.64 on OSCC and OPMDs, respectively. In comparison, DenseNet-196 yielded the best multiclass image classification performance with AUC of 1.00 and 0.98 on OSCC and OPMD, respectively. These values were inline with the performance of experts and superior to those of general practictioners (GPs). In conclusion, CNN-based models have potential for the identification of OSCC and OPMDs in oral photographic images and are expected to be a diagnostic tool to assist GPs for the early detection of oral cancer.
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