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Detection of maxillary sinus pathologies using deep learning algorithms.

Ceren Aktuna Belgin1, Aida Kurbanova2, Seçil Aksoy3

  • 1Faculty of Dentistry, Department of Dentomaxillofacial Radiology, Hatay Mustafa Kemal University, Hatay, Turkey.

European Archives of Oto-Rhino-Laryngology : Official Journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
|May 20, 2025
PubMed
Summary

Artificial intelligence (AI) accurately detects maxillary sinus pathologies using deep learning on cone beam computed tomography (CBCT) scans. This AI approach shows promise for efficient and precise clinical assessment of sinus conditions.

Keywords:
Artificial intelligenceCone beam computed tomographyConvolutional neural networkDeep learningDentistryMaxillary sinus

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Deep learning applications

Background:

  • Accurate identification of maxillary sinus pathologies is vital for successful surgical outcomes.
  • Cone beam computed tomography (CBCT) is a preferred imaging modality for maxillary sinus evaluation due to its high resolution and reduced radiation.
  • Deep learning models are increasingly applied in medical diagnostics.

Purpose of the Study:

  • To evaluate the accuracy of artificial intelligence (AI) algorithms in detecting maxillary sinus pathologies from CBCT scans.
  • To develop and assess a convolutional neural network (CNN) for automated segmentation of maxillary sinus pathologies.

Main Methods:

  • A dataset of 1000 maxillary sinuses from 500 patients underwent CBCT analysis.
  • Manual segmentation masks were created using ITK-SNAP as a reference standard.
  • A CNN model was trained for automated segmentation, with accuracy evaluated using Dice similarity coefficient (DSC) and intersection over union (IoU).

Main Results:

  • The AI model achieved a high Dice score of 0.923 and an IoU of 0.887.
  • The model demonstrated strong performance with a recall of 0.979 and an F1 score of 0.970.
  • Precision for the automated segmentation was reported at 0.963.

Conclusions:

  • An AI-driven method for segmenting maxillary sinus pathologies in CBCT images was successfully developed.
  • The study demonstrates the potential of AI for rapid and accurate clinical assessment of maxillary sinus conditions.
  • This AI approach could enhance diagnostic efficiency and treatment planning for sinus pathologies.