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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet.

Adalet Çelebi1, Andaç Imak2, Hüseyin Üzen3

  • 1Oral and Maxillofacial Surgery Department, Faculty of Dentistry, Mersin University, Mersin, Turkey.

Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
|August 26, 2023
PubMed
Summary

This study introduces Res-Swin-UNet, an AI model that accurately detects maxillary sinus infections from cone beam computed tomography (CBCT) scans, aiding dentists in diagnosis.

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Area of Science:

  • Artificial Intelligence in Dentistry
  • Medical Imaging Analysis
  • Cone Beam Computed Tomography (CBCT)

Background:

  • Accurate detection of maxillary sinus pathologies is crucial for dental diagnosis and treatment.
  • Current methods for analyzing CBCT images can be time-consuming and require specialized expertise.

Purpose of the Study:

  • To develop and evaluate an AI-based model for automated detection of pathologic conditions and infections in the maxillary sinus using CBCT images.
  • To assist dental professionals by providing precise boundary identification of sinus abnormalities.

Main Methods:

  • A novel deep learning architecture, Res-Swin-UNet, was developed, integrating ResNet and Swin transformer components.
  • The model utilizes self-attention mechanisms and a patch expanding layer for enhanced feature extraction.
  • Trained and validated on a dataset of 298 CBCT images.

Main Results:

  • The Res-Swin-UNet model demonstrated high performance, achieving 99% accuracy.
  • The model attained an F1-score of 91.72% and an Intersection over Union (IoU) of 84.71%.
  • Outperformed existing state-of-the-art models in detecting sinus pathologies.

Conclusions:

  • The proposed deep learning model effectively assists dentists in automatically identifying the boundaries of maxillary sinus infections and pathologies.
  • This AI tool can streamline diagnostic workflows and improve the accuracy of sinus condition assessment in CBCT imaging.