Deep convolutional neural networks based analysis of cephalometric radiographs for differential diagnosis of orthognathic surgery indications

Ki Sun Lee, Jae Jun Ryu, Hyon Seok Jang, Dong Yul Lee, Seok Ki Jung

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs' class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs' class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery.

Original languageEnglish
Article number2124
JournalApplied Sciences (Switzerland)
Volume10
Issue number6
DOIs
Publication statusPublished - 2020 Mar 1

Keywords

  • Artificial intelligence
  • Cephalometric radiographs
  • Convolutional neural networks
  • Orthognathic surgery

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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