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Classification of Apical Openness Using Vision Transformer: A Comparative Approach with Expert Decisions.

Merve Daldal1, Sümeyye Coşgun Baybars2, Merve Parlak Baydoğan3

  • 1Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Fırat University, Elazığ, Turkey. mdaldal@firat.edu.tr.

Journal of Imaging Informatics in Medicine
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an artificial intelligence (AI) method using a Vision Transformer (ViT) model to classify apical root openness in dental panoramic radiographs. The AI achieved 88% accuracy, offering consistent results for improved clinical decision support.

Keywords:
Apical opennessArtificial intelligence (AI)Dental imagingPanoramic radiographVision Transformer (ViT)

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Area of Science:

  • Dentistry
  • Radiology
  • Artificial Intelligence

Background:

  • Root morphology evaluation is vital for dental diagnosis and treatment planning.
  • Apical openness, indicating incomplete root development, poses challenges in endodontic and orthodontic treatments, particularly for young patients.
  • Panoramic radiographs offer broad anatomical coverage with low radiation doses, making them suitable for dental assessments.

Purpose of the Study:

  • To develop and evaluate an artificial intelligence (AI)-based method for classifying apical root openness in panoramic radiographs.
  • To assess the performance of a Vision Transformer (ViT) model in identifying different degrees of apical openness.

Main Methods:

  • A dataset of 902 single-rooted permanent teeth from 512 panoramic radiographs was curated.
  • Teeth were manually cropped and categorized into closed apex, anatomically open, and pathologically open groups.
  • A Vision Transformer (ViT Base Patch32) model was employed for classification after image preprocessing.

Main Results:

  • The ViT model achieved an overall accuracy, precision, recall, and F1-score of 88%.
  • The AI model demonstrated more consistent classification outcomes compared to manual assessments by dental students.
  • The model particularly outperformed less experienced dental professionals in classifying apical root openness.

Conclusions:

  • The Vision Transformer (ViT) model shows high accuracy in detecting apical root openness on panoramic radiographs.
  • This AI-based approach shows significant promise as a reliable tool for clinical decision support systems in dentistry.
  • The AI method can aid dentists in diagnosis and treatment planning, especially in cases involving incomplete root development.