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A deep convolutional neural network (DCNN) computer-assisted diagnosis (CAD) system accurately detects facial asymmetry on cephalograms. This DCNN system shows clinically acceptable diagnostic performance comparable to orthodontists.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in orthodontics

Background:

  • Facial asymmetry is a common concern in orthodontic diagnosis.
  • Accurate detection of facial asymmetry is crucial for effective orthodontic treatment planning.

Purpose of the Study:

  • To evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system for detecting facial asymmetry on posteroanterior (PA) cephalograms.
  • To compare the diagnostic accuracy of the DCNN system with that of an orthodontist.

Main Methods:

  • A DCNN-based CAD system was trained using PA cephalograms from 1020 orthodontic patients.
  • The system was designed to autoassess facial asymmetry, menton deviation, and landmark coordinates.
  • The DCNN's performance was tested on 25 PA cephalograms and analyzed using independent t-tests and Bland-Altman plots.

Main Results:

  • The DCNN-based CAD system demonstrated no significant differences compared to conventional analysis.
  • Bland-Altman plots indicated good agreement for all measurements between the DCNN system and conventional methods.
  • The DCNN system showed comparable diagnostic performance to orthodontists in evaluating facial asymmetry.

Conclusions:

  • The DCNN-based CAD system provides a clinically acceptable method for diagnosing facial asymmetry on PA cephalograms.
  • This AI-driven approach has the potential to assist orthodontists in routine diagnostic evaluations.
  • Further validation may confirm its utility in enhancing diagnostic efficiency and accuracy in orthodontics.