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A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images.

Çağla Sin1, Nurullah Akkaya2, Seçil Aksoy3

  • 1Faculty of Dentistry, Department of Orthodontics, Near East University, Mersin10, Turkey.

Orthodontics & Craniofacial Research
|February 23, 2021
PubMed
Summary

A new artificial intelligence (AI) algorithm accurately segments the pharyngeal airway in cone-beam computed tomography (CBCT) images. This AI system enables quick and easy pharyngeal airway volume calculation for clinical use.

Keywords:
artificial intelligencecone-beam computed tomographydeep learningpharyngeal airway

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Pharyngeal airway assessment is crucial in diagnosing conditions like sleep apnea.
  • Manual segmentation of the pharyngeal airway from cone-beam computed tomography (CBCT) images is time-consuming and subjective.
  • Developing automated methods can improve efficiency and consistency in airway analysis.

Purpose of the Study:

  • To evaluate a deep learning-based artificial intelligence (AI) system for automatic segmentation of the pharyngeal airway in CBCT images.
  • To compare the AI segmentation results with manual segmentation by human observers.
  • To assess the accuracy and clinical applicability of the AI algorithm.

Main Methods:

  • A retrospective study included 306 subjects with CBCT scans.
  • A Convolutional Neural Network (CNN) based AI algorithm was developed for pharyngeal airway segmentation.
  • Manual segmentation was performed using ITK-SNAP software for comparison.
  • Segmentation accuracy was quantified using Dice Similarity Coefficient (DSC) and Intersection over Union (IoU).

Main Results:

  • The AI algorithm achieved a Dice ratio of 0.919 and a weighted IoU of 0.993 for pharyngeal airway segmentation.
  • The average pharyngeal airway volume calculated by AI was 17.32 cm³, compared to 18.08 cm³ by human observers.
  • These results indicate high accuracy and agreement between AI and manual segmentation.

Conclusions:

  • A successful AI algorithm for automatic pharyngeal airway segmentation from CBCT images has been developed.
  • The AI system demonstrates high accuracy and efficiency in calculating pharyngeal airway volume.
  • This AI tool holds significant potential for clinical applications, aiding in faster and more reliable airway assessments.