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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Transformer and Attention Enhanced Deep Learning Approach for CBCT-Based Mental Foramen Classification and

Osman Güler1, Mustafa Teke2, Zafer Civelek3

  • 1Departmant of Electronic and Automation, Technical Sciences Vocational School, Gazi Universty, Ankara, Turkey.

Journal of Imaging Informatics in Medicine
|June 16, 2026
PubMed
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This study introduces advanced deep learning models for accurately locating and segmenting the mental foramen in cone-beam computed tomography (CBCT) images, improving dental surgical planning.

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The precise identification of the mental foramen is crucial for successful dental surgical procedures.
  • Variations in the mental foramen's anatomy can pose challenges in clinical practice.

Purpose of the Study:

  • To develop and evaluate deep learning models for automated classification and segmentation of the mental foramen.
  • To enhance the accuracy and reliability of mental foramen detection in cone-beam computed tomography (CBCT) images.

Main Methods:

  • Utilized a unique, manually annotated CBCT dataset for training and validation.
  • Developed an ensemble deep learning model (DAFNet) with Transformer and Attention mechanisms for classification.
  • Proposed a UNet-based segmentation model (TA-UNet) incorporating Transformer and Attention mechanisms.
Keywords:
Artificial intelligenceCBCTDeep learningExplainable artificial intelligenceMental foramen

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Main Results:

  • The DAFNet model achieved 95.20% validation accuracy and 91.25% test accuracy (AUC: 0.9648).
  • The TA-UNet model demonstrated a Dice score of 0.8804 and IoU of 0.8386.
  • Both models outperformed conventional deep learning architectures in detecting and segmenting the mental foramen.

Conclusions:

  • Deep learning models can automatically detect the mental foramen with high accuracy in CBCT images.
  • Reliable segmentation of the mental foramen is achievable using the proposed TA-UNet model.
  • These findings support the integration of AI in dental imaging for improved surgical planning.