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Related Experiment Video

Updated: Jun 1, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

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Automatic Vertical Root Fracture Detection on Intraoral Periapical Radiographs With Artificial Intelligence-Based

Sifa Ozsari1, Kıvanç Kamburoğlu2,3,4, Aviad Tamse4

  • 1Department of Computer Engineering, Ankara University, Ankara, Turkey.

Dental Traumatology : Official Publication of International Association for Dental Traumatology
|January 20, 2025
PubMed
Summary
This summary is machine-generated.

Transfer learning (TL) significantly improves vertical root fracture (VRF) diagnosis using AI-enhanced dental radiographs. This study shows TL models achieve high accuracy, aiding clinicians in identifying fractures more effectively.

Keywords:
PSOVRFdeep learningimage enhancement

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Area of Science:

  • Dental radiology
  • Artificial intelligence in medicine
  • Machine learning for diagnostics

Background:

  • Vertical root fractures (VRFs) pose diagnostic challenges in dentistry.
  • Accurate VRF detection is crucial for treatment planning and prognosis.
  • Current diagnostic methods may have limitations in sensitivity and specificity.

Purpose of the Study:

  • To evaluate transfer learning (TL) techniques for improving vertical root fracture (VRF) diagnosis accuracy.
  • To assess the impact of artificial intelligence (AI) on image enhancement for VRF detection.
  • To analyze VRF detection performance on both extracted teeth and intraoral radiographs.

Main Methods:

  • Utilized a dataset of 378 intraoral periapical radiographs (195 fractured, 183 control).
  • Employed DenseNet, ConvNext, Inception121, and MobileNetV2 with model fusion.
  • Applied Particle Swarm Optimization (PSO) and Deep Learning (DL) for image enhancement prior to evaluation.

Main Results:

  • The DenseNet + Inception fusion model achieved the highest accuracy (0.80), with strong recall, F1-score, and AUC.
  • Molar tooth analysis showed 0.80 accuracy, 0.84 AUC, and 0.60 kappa.
  • Premolar analysis yielded 0.78 accuracy, 0.78 AUC, and 0.55 kappa, with acceptable intra- and inter-observer agreement.

Conclusions:

  • Transfer learning (TL) demonstrates significant potential for enhancing VRF diagnostic accuracy in radiographic imaging.
  • TL is a valuable tool for developing automated diagnostic systems for VRF identification.
  • These AI-driven approaches can support clinicians in making more accurate VRF diagnoses.