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Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images.

Şuayip Burak Duman1, Ali Z Syed2, Duygu Celik Ozen1

  • 1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, 44210 Malatya, Turkey.

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Summary

Artificial intelligence accurately classifies sella turcica shapes in cone-beam computed tomography (CBCT) images. This deep learning approach enhances diagnostic efficiency for orthodontists by automating landmark detection.

Keywords:
CBCTartificial intelligenceconvolutional neural networksella turcica

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Area of Science:

  • Dentistry and Oral Health
  • Medical Imaging and Radiology
  • Artificial Intelligence in Medicine

Background:

  • Accurate morphological classification of sella turcica is crucial for orthodontic diagnosis.
  • Traditional methods for analyzing sella turcica in cone-beam computed tomography (CBCT) images can be time-consuming.
  • Deep learning offers potential for automating and improving the analysis of anatomical structures in medical imaging.

Purpose of the Study:

  • To validate the diagnostic performance of an AI system for sella turcica morphological classification using CBCT images.
  • To evaluate the reliability of convolutional neural network (CNN) models in segmenting and classifying sella turcica shapes.
  • To assess the potential of AI in streamlining diagnostic processes for orthodontists.

Main Methods:

  • A retrospective study utilizing CBCT images.
  • Application of AI-based sella segmentation and classification models (CranioCatch) employing PyTorch, U-Net, TensorFlow 1, and GoogleNet Inception V3.
  • Analysis focused on sagittal slices of CBCT images for morphological classification (flattened, oval, round).

Main Results:

  • AI models demonstrated high accuracy in sella turcica segmentation, achieving sensitivity, precision, and F-measure values of 1.0.
  • Classification performance metrics included high sensitivity, precision, and F1-scores across different sella turcica shapes.
  • Specific metrics for flattened, oval, and round classifications showed robust performance, indicating reliable AI diagnostic capabilities.

Conclusions:

  • The AI system exhibits strong diagnostic performance and reliability for sella turcica morphological classification in CBCT images.
  • Deep learning models effectively segment and classify sella turcica, supporting orthodontic diagnosis.
  • AI-driven detection of orthodontic landmarks like the sella point promises to save orthodontists time and enhance diagnostic accuracy.