Assessment of Automatic Cephalometric Landmarks Identification Using Artificial Intelligence

Hypothesis and Objective: To compare the accuracy of cephalometric landmark identification between Artificial Intelligence (AI) Deep Learning Convolutional Neural Networks YOLOv3 algorithm and Manually Traced (MT) group. Methods: AAOF Legacy Denver collection was used to obtain 110 cephalometric images for this study. Lateral cephalograms were digitized by orthodontic resident in Dolphin Imaging after intra- and inter reliability check. The same images were uploaded to AI software Ceppro DDH Inc. Cartesian system of coordinates with Sela as 0:0 was used to extract x and y coordinate for 16 cephalometric points: Nasion, A point, B point, Menton, Gonion, Upper incisor tip, Lower incisor tip, Upper incisor apex, Lower incisor apex, Anterior Nasal Spine, Posterior Nasal Spine, Pogonion, Pterigomaxillary fissure point, Basion, Articulare and Orbitale. The mean distances were assessed relative to the reference value of 2 mms. Student paired t-tests at significance level of 5 % were used to compare the mean differences in each of the X- and Y-components. SPSS (IBM-vs. 27.0) software was use for the data analysis. Results: The results showed that there is no statistical difference for 12 out of 16 points when analyzing absolute difference between MT and AI group. Success detection rate for AI within 2 mm while comparing MT and AI group was 75 % and 93% within 4 mm. Conclusions: AI could be considered a promising tool to facilitate cephalometric tracing process in routine clinical practice and in research settings. Funding: no funding. IRB and/or ACC Protocol #: 2019-1180. Notice of Determination: activity Does Not Represent Human Subjects Research.