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Classification of Intraoral Photographs with Deep Learning Algorithms Trained According to Cephalometric

Sultan Büşra Ay Kartbak1, Mehmet Birol Özel1, Duygu Nur Cesur Kocakaya2

  • 1Department of Orthodontics, Faculty of Dentistry, Kocaeli University, Kocaeli 41190, Türkiye.

Diagnostics (Basel, Switzerland)
|May 14, 2025
PubMed
Summary

Deep learning models can classify intraoral photographs using cephalometric measurements, potentially reducing the need for traditional radiographs in orthodontic analysis. This AI approach shows promising results for diagnosis and treatment planning.

Keywords:
artificial intelligencecephalometrydeep learningintraoral photograph

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Area of Science:

  • Orthodontics
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Intraoral photographs are crucial for orthodontic diagnosis, treatment planning, and documentation.
  • Evaluating deep learning (DL) algorithms for classifying intraoral photographs based on cephalometric measurements is essential.

Purpose of the Study:

  • To assess the efficacy of DL algorithms trained with cephalometric data for classifying intraoral clinical photographs.
  • To determine the potential of AI in orthodontic analysis, possibly negating the need for lateral cephalometric radiographs.

Main Methods:

  • Utilized lateral cephalograms and intraoral images from 990 patients.
  • Measured IMPA, interincisal angle, U1-palatal plane angle, and Wits appraisal using WebCeph.
  • Trained 14 DL models (e.g., DenseNet, EfficientNet, ResNet) to classify intraoral photographs into three groups based on cephalometric measurements.

Main Results:

  • High accuracy rates achieved: up to 98.33% for IMPA, 99.00% for interincisal angle, 96.67% for U1-palatal plane angle, and 98.33% for Wits appraisal.
  • Varied accuracy observed across classifications and DL algorithms, with some lower rates (e.g., 33.33% for U1-palatal plane angle).
  • Successful classification was demonstrated in the majority of cases by the DL models.

Conclusions:

  • Deep learning algorithms demonstrate capability in classifying intraoral photographs based on cephalometric data.
  • The findings suggest a promising future for AI in orthodontic case classification and analysis.
  • This AI-driven approach may reduce reliance on conventional lateral cephalometric radiography.