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Accuracy of deep learning-based upper airway segmentation.

Yağızalp Süküt1, Ebru Yurdakurban2, Gökhan Serhat Duran3

  • 1Department of Orthodontics, Gülhane Faculty of Dentistry, University of Health Sciences, Ankara 06010, Turkey.

Journal of Stomatology, Oral and Maxillofacial Surgery
|September 7, 2024
PubMed
Summary

Both automatic and semi-automatic methods for segmenting upper airway volume from cone beam computed tomography (CBCT) scans show clinically acceptable accuracy. These open-source tools offer efficient and comparable results to manual segmentation for orthodontic treatment planning.

Keywords:
Artificial intelligence (AI)Cone-beam computed tomography (CBCT)Convolutional neural networks (CNNs)Upper airway segmentation

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Area of Science:

  • Medical Imaging
  • Orthodontics
  • Computational Anatomy

Background:

  • Accurate assessment of upper airway volume and morphology is crucial for orthodontic diagnosis and treatment planning.
  • Cone beam computed tomography (CBCT) is a key imaging modality for evaluating the upper airway.
  • Segmentation of the upper airway from CBCT data can be performed using manual, semi-automatic, or automatic methods.

Purpose of the Study:

  • To compare the accuracy of an automatic upper airway segmentation model against a semi-automatic method and manual segmentation.
  • To evaluate the clinical applicability of open-source tools for upper airway analysis in orthodontics.

Main Methods:

  • An automatic segmentation model was developed using the MONAI Label framework.
  • Semi-automatic segmentation was performed using ITK-SNAP.
  • Accuracy was assessed against manual segmentation using Dice Similarity Coefficient (DSC), Precision, Recall, 95% Hausdorff Distance (HD), and volumetric differences.

Main Results:

  • Both automatic (DSC: 0.915±0.041) and semi-automatic (DSC: 0.940±0.021) methods demonstrated clinically acceptable accuracy.
  • Semi-automatic segmentation showed higher accuracy (95% HD: 0.997±0.585) compared to automatic segmentation (95% HD: 1.447±0.674).
  • No statistically significant volumetric differences were found between automatic/semi-automatic and manual methods for total, oropharyngeal, and velopharyngeal airway volumes.

Conclusions:

  • Both open-source automatic and semi-automatic methods provide accurate and efficient upper airway segmentation comparable to manual segmentation.
  • These methods can aid orthodontic decision-making by streamlining the segmentation process.
  • Implementation of these tools can enhance diagnostic capabilities in orthodontic practice.