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Performance evaluation of deep learning models for overbite classification on cephalometric radiographs.

Sultan Büşra Ay Kartbak1, Mehmet Birol Özel1, Muhammet Çakmak2

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Summary

Deep learning models effectively classify overbite from cephalometric X-rays, with EfficientNet B0 and Hybrid models showing top performance. This approach offers a reliable alternative to traditional cephalometric analysis for overbite categorization.

Keywords:
artificial intelligencecephalometrydeep learningorthodontic assessmentoverbite

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Area of Science:

  • Orthodontics and Dental Imaging
  • Artificial Intelligence in Healthcare
  • Medical Image Analysis

Background:

  • Overbite classification is crucial in orthodontics.
  • Traditional cephalometric analysis can be time-consuming.
  • Deep learning offers potential for automated image analysis.

Purpose of the Study:

  • To evaluate and compare deep learning algorithms for overbite classification.
  • To assess the effectiveness of various models on lateral cephalometric radiographs.
  • To determine the potential of AI in replacing conventional cephalometric analysis.

Main Methods:

  • Utilized lateral cephalometric radiographs from 1062 patients.
  • Categorized radiographs into Overbite 1, 2, and 3 groups.
  • Employed six deep learning models (ResNet101, DenseNet201, EfficientNetV2-B0, ConvNetBase, EfficientNet-B0, Hybrid Model).
  • Evaluated performance using accuracy, F1-score, MAE, Kappa, and AUC-ROC.

Main Results:

  • All models achieved over 85% classification accuracy.
  • EfficientNet B0 and Hybrid models demonstrated the highest accuracy.
  • ConvNetBase model showed the lowest classification accuracy.
  • Confusion matrices and Grad-CAM visualizations aided interpretation.

Conclusions:

  • Deep learning models can accurately classify cephalometric overbite categories.
  • This AI-driven approach may eliminate the need for manual cephalometric analysis.
  • The study highlights the potential of deep learning in orthodontic diagnostics.