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A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.
Yujia Jiang1, Jiayi Jin2, Yunru Gao2
1School of Stomatology, Nanjing Medical University, Nanjing, Jiangsu, China.
International Dental Journal
|March 1, 2026
Summary
A new deep learning system, SMM-YOLOv8n, accurately detects pathologies like caries and root resorption in second molars adjacent to impacted third molars on dental radiographs. This tool enhances diagnostic speed and accuracy, aiding treatment planning.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Imaging Analysis
- Oral Pathology Detection
Background:
- Impacted third molars (ITMs) can cause pathologies in adjacent second molars (M2s).
- Accurate detection of these M2 pathologies on panoramic radiographs is crucial for timely intervention.
- Current diagnostic methods may have limitations in speed and accuracy.
Purpose of the Study:
- To develop and evaluate an automated deep learning (DL) system for detecting and classifying M2 pathologies associated with ITMs.
- To improve diagnostic accuracy and efficiency compared to existing methods.
- To support clinical decision-making in managing ITM-related complications.
Main Methods:
- A dataset of 1,170 panoramic radiographs with M2s adjacent to ITMs was curated.
- Images were classified into four groups: no lesions, caries, external root resorption (ERR), or both.
- An enhanced SMM-YOLOv8n model, based on YOLOv8 with attention mechanisms, was developed and trained using transfer learning.
- The model was evaluated using 5-fold cross-validation.
Main Results:
- The SMM-YOLOv8n model achieved a mean Average Precision (mAP@50) of 0.886.
- Macro-averaged precision, recall, and F1-score were 0.894, 0.96, and 0.926, respectively.
- Compared to baseline YOLOv8, the system demonstrated significant improvements, increasing clinician sensitivity and reducing interpretation time.
Conclusions:
- The SMM-YOLOv8n system provides accurate and efficient detection of ITM-related M2 pathologies on panoramic radiographs.
- This DL tool can enhance early diagnosis and treatment planning.
- It may reduce the reliance on cone-beam computed tomography for initial assessments and serve as a valuable decision-support system.
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