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

Abstract

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.

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“…Popular keywords found in the studies included "deep learning," "machine learning," "convolutional neural network," and "automated identification,", highlighting the significant interest in these AI technologies. Notable examples of automated tracing and landmark identification systems, such as CephX (ORCA Dental AI, Israel) and WebCeph (AssembleCircle, South Korea) [ 30 , 31 ]. Furthermore, AI algorithms can remove noise, enhance contrast and fine-tune images to provide dentists with clearer radiographs [ 32 ].…”

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

“…In general, acceptable linear and angular measurement are less than 2 mm and 2 • , respectively [23,36,38,43,44,47,54,[66][67][68][69][70]. Following this criterion, although some commercially available software can achieve high overall accuracy in automated landmarking on lateral cephalograms, manual supervision is still recommended [47,48,53,54,56].…”

Evaluation of the Effectiveness of Orthopedic Face Mask Treatment Using Artificial Intelligence on Two-Dimensional Photographs

“…For this study, an individual account was created on the WebCeph website (31)(32)(33). Photographs were taken of all participants, and the Frankfurt horizontal plane and the line passing through the pupils in a frontal view were both drawn parallel to the ground.…”

“…Further, Çoban et al used AI while determining the location of the cephalometric points and stated that despite there being important differences between the cephalometric points determined by AI and those determined by an author of the study, AI is still useful for making a quick preliminary clinical evaluation (19). In addition, Duran et al reported that the AI-assisted automatic analysis gave similar results with the non-automatic analysis type, according to their study using a similar software (33).…”