Evaluation of an Automatic Cephalometric Superimposition Method Based on Feature Matching.

Abstract

The objective of the study is to establish a novel method for automatic cephalometric superimposition on the basis of feature matching and compare it with the commonly used Sella-Nasion (SN) superimposition method. A total of 178 pairs of pre- (T1) and post-treatment (T2) lateral cephalometric radiographs (LCRs) from adult orthodontic patients were collected. Ninety LCR pairs were used to train the you only look once version 8 (YOLOv8) model to automatically recognize stable cranial reference areas. This approach represents a novel method for automated superimposition on the basis of feature matching. The remaining 88 LCR pairs were used for landmark identification by three orthodontic experts to evaluate the accuracy of the two superimposition methods. The Euclidean distances of 17 hard tissue landmarks were measured and statistically compared after superimposition. Significant differences were observed in the superimposition error of most landmarks between the two methods (p < 0.05). The successful detection rate (SDR) of automatic superimposition of each landmark within the precision ranges of 1 mm, 2 mm, and 3 mm via the new method was higher than that via the SN method. The new automatic superimposition method is more accurate than the SN method and is a reliable method for superimposing adult LCRs, thus providing support for clinical or research work.