ArticleBMC oral health2025
A deep learning approach based on YOLO v11 for automatic detection of jaw cysts.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SBRF-Det: Scale-Boundary Representative Fusion for Multi-Finding Detection in Panoramic Dental Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.International dental journal · 2026Article
- Different Approaches to the Treatment of Radicular and Related Cysts Associated with Nasal Mucosa in the Maxilla: A Case Series.Journal of clinical medicine · 2026Article
- "Dry Tap" Fine-Needle Aspiration Biopsy as a Diagnostic Clue in Cyst-like Juvenile Jaw Lesions Mimicking Dentigerous Cysts on Panoramic Radiography and Cone-Beam Computed Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence for the detection and diagnosis of oral and maxillofacial lesions: evidence, limitations, and future directions.Brazilian oral research · 2026Review
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Authors and funding
6 authors.
Funding
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Abstract
objectiveJaw cysts are frequent radiolucent lesions in dentistry that can present diagnostic difficulties due to their similar radiographic appearance. This study aimed to develop an AI-based detection and classification system for jaw cysts using the YOLO v11 deep learning model on panoramic radiographs. MATERIALS AND
methodsA total of 311 panoramic images (211 cystic, 100 normal) were labeled and augmented. The YOLO v11 Small model was trained in both multi-class (distinguishing between dentigerous cysts [DCs], odontogenic keratocysts [OKCs], and radicular cysts [RCs]) and single-class configurations (detecting cysts without type differentiation). Performance metrics included precision, recall, F1 score, and mean average precision (mAP).
resultsIn the multi-class model, the system achieved an mAP of 86%, with precision of 84%, recall of 82%, and F1 score of 83%. Class-wise mean accuracies were 91% for DCs, 85% for OKCs, and 82% for RCs. The single-class model showed slightly lower performance with an mAP of 84% and F1 score of 81%.
conclusionYOLO v11 demonstrated high accuracy in detecting jaw cysts, indicating its potential to support dental diagnostics. Further validation on larger and balanced datasets is recommended to enhance generalizability.
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