![]() Manual tracings were performed by 3 operators. Correction of CephX landmarks was also made. Eight angular and 4 linear parameters were measured by 1 operator using 3 methods: computerized tracing with software Dolphin Imaging 13.01(Dolphin Imaging and Management Solutions, Chatsworth, Calif, USA), app-aided tracing using the CephNinja 3.51 app (Cyncronus LLC, WA, USA), and web-based fully automated tracing with CephX (ORCA Dental AI, Las Vegas, NV). Pre-treatment lateral cephalometric radiographs of 40 patients were randomly selected. To compare the accuracy of cephalometric analyses made with fully automated tracings, computerized tracing, and app-aided tracings with equivalent hand-traced measurements, and to evaluate the tracing time for each cephalometric analysis method. The AI-based cephalometric analysis method needs to be developed for more specific malocclusions. For both methods, all parameters except CoA and CoGn were found to have good correlation.Īlthough significant differences were detected in some measurements between the two cephalometric analysis methods, not all differences were clinically significant. The ANB angle differed significantly in all three malocclusion groups. Meanwhile, only the SNA in the Class III malocclusion group was different (p< 0.05). The level of statistical significance was set at p 0.05) in both SNA and SNB measurements between the two methods in the Class I malocclusion group, there was a difference between both methods in the Class II malocclusion group. ![]() The paired t-test, one-way ANOVA test, and intraclass correlation coefficient tests were used to evaluate the differences between the two methods. ![]() ![]() In total, 10 linear and 12 angular measurements were evaluated. Dolphin Imaging software was used for DM cephalometric analysis, and the WebCeph platform was used for AI-based cephalometric analysis. The aim of this study was to compare the measurements performed with digital manual (DM) cephalometric analysis and automatic cephalometric analysis obtained from an online artificial intelligence (AI) platform, according to different sagittal skeletal malocclusions.Ĭephalometric radiographs of 105 randomly selected individuals (mean age: 17.25 ± 1.87 years) were included in this study. ![]()
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