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.
The recent study aimed to evaluate the knowledge levels of dentists on intraoral scanners and 3D printers with their usage prevalence of them in Türkiye.A link to the survey was sent via e-mail to 1782 dentists in different branches who participated in this survey, which had 27 questions in four subgroups, including demographic information, general digital dentistry, intraoral scanners, and 3D printers. The total number of dentists who answered the questionnaire was determined as 305. Considering the distribution of the ages, %21.3 (n=65) of the participants were 23-28 years, %32.1 (n=98) were 29-35 years, and %46.6 (n=142) were 36 and over years. A statistically significant difference was found in the use of intraoral scanners and 3D printers in workplaces (p<0.001, p<0.001). A statistically significant difference was found regarding branches in intraoral scanner usage. While the rate of using intraoral scanners by 54.5% of restorative dentists is higher than other branches (p<0.001), orthodontists were found to be more active users of 3D printers (47.9%, p=0.001). According to the results of our study, it has been observed that dentists generally obtain information about intraoral scanners and 3D printers through individual efforts and only a few dentists in Türkiye can integrate these devices into their everyday practices.
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