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Related Experiment Video

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Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a

Hwangyu Lee1, Jung Min Cho1, Susie Ryu2

  • 1Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.

Scientific Reports
|September 19, 2023
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Summary

A new artificial intelligence (AI) model for automatic posteroanterior (PA) cephalometric landmark identification demonstrates accuracy comparable to human experts. This AI tool promises to enhance efficiency in cephalometric analysis for clinicians.

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cephalometric analysis is crucial in orthodontics and maxillofacial surgery.
  • Manual landmark identification is time-consuming and prone to inter-observer variability.
  • Automated methods can potentially improve efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate a fully automatic deep learning model for posteroanterior (PA) cephalometric landmark identification.
  • To compare the accuracy and reliability of the AI model against expert human examiners.

Main Methods:

  • A deep learning algorithm was trained and validated on 1032 PA cephalometric images.
  • 19 landmarks were automatically identified by the AI model on 82 test images.
  • Performance was assessed using Mean Radial Error (MRE) and Successful Detection Rate (SDR), compared to manual identification by two expert examiners.

Main Results:

  • The AI model's performance was comparable to that of human experts.
  • The model achieved an MRE of 1.87 ± 1.53 mm.
  • Successful Detection Rates were 34.7% (<1.0 mm), 67.5% (<2.0 mm), and 91.5% (<4.0 mm).
  • Sphenoid and mastoid landmarks showed higher accuracy; condyle landmarks showed lower accuracy.

Conclusions:

  • The fully automatic PA cephalometric landmark identification model exhibits promising accuracy and reliability.
  • This AI tool can significantly improve the efficiency of cephalometric analysis, saving clinicians time and effort.
  • Further AI advancements are expected to enhance model accuracy and efficiency.