ArticleInternational dental journal2026
A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.
Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Vulnerability of third molars to internal resorption: two case reports ofInternational journal of surgery case reports · 2026Article
- Transformer and generative models in dental and maxillofacial imaging: Narrative review of clinical evidence and future directions.Imaging science in dentistry · 2026Review
- Comparative Evaluation of Hybrid Attention-CNN and Vision Transformer Models for Multi-Class Classification of Third-Second Molar Relationships on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to develop an automated deep learning (DL) system to detect and classify second molar (M2) pathologies associated with impacted third molars (ITMs) on panoramic radiographs. We sought to enhance diagnostic accuracy and support informed clinical decision-making.
methodsWe constructed a dataset of 1,170 panoramic radiographs that show second molars (M2s) adjacent to impacted third molars (ITMs). The cases were retrospectively divided into 4 groups: (1) no lesions; (2) caries (tooth decay); (3) external root resorption (ERR; loss of tooth root structure from external factors); or (4) both pathologies. Three oral surgeons, each with extensive experience, annotated the images using standardized criteria, resolving any disagreements by consensus. Our enhanced SMM-YOLOv8n model is based on You Only Look Once version 8 (YOLOv8) and features Slim-Neck optimization and multidimensional attention mechanisms. We trained the model with transfer learning (applying knowledge from pre-trained models to new tasks). We evaluated it using 5-fold cross-validation (dividing the data into 5 parts and rotating the validation sets).
resultsSMM-YOLOv8n achieved an mAP@50 of 0.886 on the internal test set. The macro-averaged precision, recall, and F1-score were 0.894, 0.96, and 0.926 (calculated at an IoU threshold of 0.5). These results show clear improvement over the baseline YOLOv8. For 60 images, the clinicians' mean sensitivity increased by 0.171, and the average interpretation time decreased by 8.79 minutes.
conclusionsSMM-YOLOv8n offers accurate and efficient detection of ITM-related M2 pathologies on panoramic radiographs. This approach may enhance early diagnosis, aid in treatment planning, and reduce the need for cone-beam computed tomography in initial assessments. CLINICAL SIGNIFICANCE: This DL-based diagnostic tool may serve as a valuable decision-support system, particularly in clinical settings with limited access to specialized dental expertise.
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