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Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs.

Deniz Bora Küçük1, Andaç Imak2, Salih Taha Alperen Özçelik3

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Summary
This summary is machine-generated.

An artificial intelligence (AI) model combining YOLO and RT-DETR significantly improves impacted tooth detection in dental panoramic radiographs. This AI solution enhances diagnostic accuracy and efficiency, reducing manual interpretation errors.

Keywords:
Weighted Boxes FusionYOLOimpacted tooth detectionsuper resolutiontransformer

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Digital imaging, particularly panoramic radiographs, is vital for detecting impacted teeth in dentistry.
  • Manual interpretation of these radiographs is time-consuming and prone to errors.
  • Automated solutions are needed to improve accuracy and efficiency in impacted tooth detection.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-based model for accurate and reliable detection of impacted teeth in panoramic radiographs.
  • To enhance diagnostic accuracy and efficiency in dental imaging interpretation.

Main Methods:

  • A novel AI model integrating YOLO (You Only Look Once) and RT-DETR (Real-Time Detection Transformer) was developed.
  • The model was optimized using Weighted Boxes Fusion (WBF) with Bayesian optimization for parameter tuning.
  • Performance was evaluated on a dataset of 407 labeled panoramic radiographs.

Main Results:

  • The AI model achieved a mean average precision (mAP) of 98.3% and an F1 score of 96%.
  • The proposed model demonstrated superior performance compared to individual models and other combinations.
  • Enhanced sensitivity and minimized false positive rates were observed, indicating high diagnostic accuracy.

Conclusions:

  • The study presents a scalable and reliable AI-based solution for detecting impacted teeth in panoramic radiographs.
  • The AI model offers substantial improvements in diagnostic accuracy and efficiency for clinical dentistry.
  • Future work will focus on expanding the dataset to improve model generalizability.