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Deep learning-based identification, classification and segmentation of infraosseous periodontal defects using a
Xukai Gu1, Petra Orosz1, Kristof Somodi1
1Department of Periodontology, Semmelweis University, Szentkirályi Street 47, Budapest, 1088, Hungary.
BMC Oral Health
|June 24, 2026
Summary
A new YOLOv8 model can identify, classify, and segment periodontal defects from radiographs. This artificial intelligence approach shows promise for improving diagnostic accuracy in periodontology.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Imaging Analysis
- Periodontal Disease Diagnostics
Background:
- Intrabony defects significantly impact tooth prognosis and treatment planning.
- Traditional 2D radiographs present limitations due to structural superimposition, potentially leading to misdiagnosis.
- Accurate identification and classification of periodontal defects are crucial for effective patient care.
Purpose of the Study:
- To develop and evaluate a YOLOv8 artificial intelligence model for the identification, classification, and segmentation of periodontal defects.
- To assess the performance of the YOLOv8 model in analyzing periapical radiographs.
Main Methods:
- A dataset of 329 periapical radiographs was utilized.
- Radiographs were preprocessed and manually annotated.
- The YOLOv8 neural network was trained and evaluated using metrics including AUC-ROC, F1-score, IoU, and DSC.
Main Results:
- The YOLOv8 model achieved an AUC-ROC of 0.8078.
- Macro-average F1-score was 0.6559, with average IoU and DSC values of 0.6881 and 0.7789, respectively.
- High spatial overlap (DSC > 0.86) was noted for one-, three-, and four-wall defects.
Conclusions:
- The YOLOv8 model effectively identifies, classifies, and segments infraosseous periodontal defects.
- The model demonstrates good discriminative power and moderate classification/segmentation performance.
- Further enhancement requires larger, refined training datasets to improve model accuracy.
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