Verification of Convolutional Neural Network Cephalometric Landmark Identification

• Published in: Applied Sciences
• Main Authors: Moshe Davidovitch, Tatiana Sella-Tunis, Liat Abramovicz, Shoshana Reiter, Shlomo Matalon, Nir Shpack
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: artificial intelligence; convolutional neural networks; lateral cephalometric radiographs; diagnostics; Technology; T; Engineering (General). Civil engineering (General); TA1-2040; Biology (General); QH301-705.5; Physics; QC1-999; Chemistry; QD1-999; Orca
• Online Access: https://doi.org/10.3390/app122412784; https://doaj.org/article/f05f0dbf1e184d16be28b720fdd95ea7

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.