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Profile Photograph Classification Performance of Deep Learning Algorithms Trained Using Cephalometric Measurements: A

Duygu Nur Cesur Kocakaya1, Mehmet Birol Özel1, Sultan Büşra Ay Kartbak1

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

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Deep learning algorithms can accurately classify orthodontic patient profiles using photographs, achieving over 97% accuracy. This AI approach may reduce the need for traditional cephalometric X-rays in diagnosis.

Keywords:
artificial intelligencecephalometrydeep learningorthodonticsprofile photograph

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Area of Science:

  • Orthodontics
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Extraoral profile photographs are vital for orthodontic diagnosis and treatment planning.
  • Current methods often rely on cephalometric radiography, which involves radiation exposure.
  • Developing AI-driven alternatives can enhance diagnostic efficiency and patient safety.

Purpose of the Study:

  • To assess the efficacy of deep learning algorithms in classifying orthodontic patients based on extraoral photographs.
  • To correlate photographic classifications with established cephalometric measurements.
  • To explore AI's potential to minimize reliance on cephalometric X-rays.

Main Methods:

  • Utilized cephalometric radiographs and profile photographs of 990 orthodontic patients.
  • Performed cephalometric measurements (FH-NA, FH-NPog, FMA, N-A-Pog) using Webceph software.
  • Trained 14 deep learning models on patient photographs grouped by cephalometric values.

Main Results:

  • Achieved high accuracy rates in classifying patient images: up to 96.67% for FH-NA, 97.33% for FH-NPog, 97.67% for FMA, and 97.00% for N-A-Pog.
  • Demonstrated strong predictive performance of the trained deep learning models.
  • Validated the correlation between cephalometric data and AI-based photographic classification.

Conclusions:

  • Deep learning models trained on cephalometric data show high accuracy in classifying orthodontic patient profiles from photographs.
  • This AI approach presents a promising, potentially radiation-free alternative for orthodontic diagnosis and treatment planning.
  • This study pioneers the use of AI for classifying clinical images based on objective cephalometric parameters.