Evaluation of the accuracy of fully automatic cephalometric analysis software with artificial intelligence algorithm

• Published in: Orthodontics & craniofacial research Vol. 26; no. 3; pp. 481 - 490
• Main Authors: Duran, Gökhan Serhat, Gökmen, Şule, Topsakal, Kübra Gülnur, Görgülü, Serkan
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.08.2023
• Subjects: Algorithms; Artificial Intelligence; automatic landmark detection; Automation; cephalometric analysis; Cephalometry - methods; Orthodontics; Radiography; Reproducibility of Results; Retrospective Studies; Software; Statistical analysis; cephalometric analysis; automatic landmark detection; artificial intelligence
• ISSN: 1601-6335; 1601-6343
• DOI: 10.1111/ocr.12633

Objective The aim of this study is to evaluate whether fully automatic cephalometric analysis software with artificial intelligence algorithms is as accurate as non‐automated cephalometric analysis software for clinical diagnosis and research. Materials and Methods This is a retrospective archive study using lateral cephalometric radiographs taken from individuals aged 12‐20 years. Cephalometric measurement data were obtained from these lateral cephalometric radiographs by manual landmark marking with non‐automated computer software (Dolphin 11.8). Again, the same radiographs were made using fully automatic digital cephalometric analysis software OrthoDx™ (AI‐Powered Orthodontic Imaging System, Phimentum) and WebCeph (Assemblecircle, Seoul, Korea) with artificial intelligence algorithm, without manual intervention of the researcher and fully automatic markings and measurements were made by the software. Results According to the consistency test, a statistically significant good level of consistency was found between Dolphin and OrthoDx™ measurements and Dolphin and WebCeph measurements in angular measurements (ICC > 0.75, P < .01, ICC > 0.75, P < 0, respectively. 01). A weak level of consistency was found in linear measurement and soft tissue parameters in both software (ICC < 0.50, P < .05, ICC < 0.50, P < .05), and the difference between measurements was statistically found to be different from “0.” Conclusion The results obtained from fully automatic cephalometric analysis software with artificial intelligence algorithms are similar to the results of non‐automated cephalometric analysis software, although there are differences in some parameters. To minimize the margin of error in artificial intelligence‐based fully automatic cephalometric software, the manual intervention of the observer is needed.