Verification of Convolutional Neural Network Cephalometric Landmark Identification

Moshe Davidovitch*, Tatiana Sella-Tunis, Liat Abramovicz, Shoshana Reiter, Shlomo Matalon, Nir Shpack

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: The mass-harvesting of digitized medical data has prompted their use as a clinical and research tool. The purpose of this study was to compare the accuracy and reliability of artificial intelligence derived cephalometric landmark identification with that of human observers. Methods: Ten pre-treatment digital lateral cephalometric radiographs were randomly selected from a university post-graduate clinic. The x- and y-coordinates of 21 (i.e., 42 points) hard and soft tissue landmarks were identified by 6 specialists, 19 residents, 4 imaging technicians, and a commercially available convolutional neural network artificial intelligence platform (CephX, Orca Dental, Hertzylia, Israel). Wilcoxon, Spearman and Bartlett tests were performed to compare agreement of human and AI landmark identification. Results: Six x- or y-coordinates (14.28%) were found to be statistically different, with only one being outside the 2 mm range of acceptable error, and with 97.6% of coordinates found to be within this range. Conclusions: The use of convolutional neural network artificial intelligence as a tool for cephalometric landmark identification was found to be highly accurate and can serve as an aid in orthodontic diagnosis.

Original languageEnglish
Article number12784
JournalApplied Sciences (Switzerland)
Volume12
Issue number24
DOIs
StatePublished - Dec 2022

Keywords

  • artificial intelligence
  • convolutional neural networks
  • diagnostics
  • lateral cephalometric radiographs

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