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Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks.

Haizhen Li1, Ying Xu1, Yi Lei2

  • 1Department of Orthodontics, Stomatology School and Hospital of Peking University, Beijing 100081, China.

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Summary

Deep learning models, specifically convolutional neural networks (CNNs), effectively classify sagittal skeletal patterns from cephalometric radiographs. DenseNet161 achieved the highest accuracy, aiding orthodontic diagnosis.

Keywords:
artificial intelligenceconvolutional neural networksorthodontic sagittal skeletal pattern classification

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Area of Science:

  • Orthodontics
  • Artificial Intelligence in Medicine
  • Radiographic Analysis

Background:

  • Sagittal skeletal patterns are crucial for orthodontic diagnosis.
  • Traditional classification methods can be time-consuming.
  • Automated classification using artificial intelligence offers potential efficiency gains.

Purpose of the Study:

  • To evaluate and compare the performance of different convolutional neural networks (CNNs) for classifying sagittal skeletal patterns.
  • To assess the diagnostic accuracy of various CNN architectures.

Main Methods:

  • A dataset of 2432 lateral cephalometric radiographs was utilized.
  • Radiographs were classified into Class I, II, and III patterns based on ANB and Wits values.
  • Four CNN models (VGG16, GoogLeNet, ResNet152, DenseNet161) were trained and compared.

Main Results:

  • DenseNet161 demonstrated the highest classification accuracy, followed by ResNet152, VGG16, and GoogLeNet.
  • GoogLeNet had the smallest model size and fastest inference speed.
  • CNNs exhibited superior performance in identifying Class III patterns and recognized compensatory dental features.

Conclusions:

  • Convolutional neural networks (CNNs) provide a rapid and effective tool for assisting orthodontists in diagnosing sagittal skeletal patterns.
  • AI-powered analysis can enhance diagnostic efficiency and accuracy in orthodontics.