Literature DB >> 33378429

Evaluation of an automated superimposition method for computer-aided cephalometrics.

Jun-Ho Moon, Hye-Won Hwang, Shin-Jae Lee.   

Abstract

OBJECTIVES: To evaluate a new superimposition method compatible with computer-aided cephalometrics and to compare superimposition error to that of the conventional Sella-Nasion (SN) superimposition method.
MATERIALS AND METHODS: A total of 283 lateral cephalometric radiographs were collected and cephalometric landmark identification was performed twice by the same examiner at a 3-month interval. The second tracing was superimposed on the first tracing by both the SN superimposition method and the new, proposed method. The proposed method not only relied on SN landmarks but also minimized the differences between four additional landmarks: Porion, Orbitale, Basion, and Pterygoid. The errors between the landmarks of the duplicate tracings oriented by the two superimposition methods were calculated at Anterior Nasal Spine, Point A, Point B, Pogonion, and Gonion. The paired t-test was used to find any statistical difference in the superimposition errors by the two superimposition methods and to investigate whether there existed clinically significant differences between the two methods.
RESULTS: The proposed method demonstrated smaller superimposition errors than did the conventional SN superimposition method. When comparisons between the two superimposition methods were made with a 1-mm error range, there were clinically significant differences between them.
CONCLUSIONS: The proposed method that was compatible with computer-aided cephalometrics might be a reliable superimposition method for superimposing serial cephalometric images.
© 2020 by The EH Angle Education and Research Foundation, Inc.

Keywords:  Automated superimposition method; Cephalometrics; Duplicate images; Error study; Sella-Nasion line

Mesh:

Year:  2020        PMID: 33378429      PMCID: PMC8032307          DOI: 10.2319/071319-469.1

Source DB:  PubMed          Journal:  Angle Orthod        ISSN: 0003-3219            Impact factor:   2.079


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