Assessment of automatic cephalometric landmark identification using artificial intelligence

Abstract

Objective: To compare the accuracy of cephalometric landmark identification between artificial intelligence (AI) deep learning convolutional neural networks (CNN) You Only Look Once, Version 3 (YOLOv3) algorithm and the manually traced (MT) group.

Setting and sample population: The American Association of Orthodontists Federation (AAOF) Legacy Denver collection was used to obtain 110 cephalometric images for this study.

Materials and methods: Lateral cephalograms were digitized and traced by a calibrated senior orthodontic resident using Dolphin Imaging. The same images were uploaded to AI software Ceppro DDH Inc The Cartesian system of coordinates with Sella as the reference landmark was used to extract x- and y-coordinates for 16 cephalometric points: Nasion (Na), A point, B point, Menton (Me), Gonion (Go), Upper incisor tip, Lower incisor tip, Upper incisor apex, Lower incisor apex, Anterior Nasal Spine (ANS), Posterior Nasal Spine (PNS), Pogonion (Pg), Pterigomaxillary fissure point (Pt), Basion (Ba), Articulare (Art) and Orbitale (Or). The mean distances were assessed relative to the reference value of 2 mm. Student paired t-tests at significance level of P < .05 were used to compare the mean differences in each of the x- and y-components. SPSS (IBM-vs. 27.0) software was used for the data analysis.

Results: There was no statistical difference for 12 out of 16 points when analysing absolute differences between MT and AI groups.

Conclusion: AI may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and in research settings.

Keywords: artificial intelligence; automated cephalometry; landmark identification.