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

2 Scopus citations


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)
Issue number24
StatePublished - Dec 2022


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


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