ArticleThe Angle orthodontist2020
Evaluation of an automated superimposition method for computer-aided cephalometrics.
Article in The Angle orthodontist, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evaluation of an Automatic Cephalometric Superimposition Method Based on Feature Matching.Journal of imaging informatics in medicine · 2025Article
- Development and validation of a graph convolutional network (GCN)-based automatic superimposition method for maxillary digital dental models (MDMs).The Angle orthodontist · 2025Article
- Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection.The Angle orthodontist · 2025Article
- Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.The Angle orthodontist · 2024Article
- Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?The Angle orthodontist · 2024Article
- Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.The Angle orthodontist · 2024Article
- Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.The Angle orthodontist · 2024Article
- Precision and accuracy of craniofacial growth and orthodontic treatment evaluation by digital image correlation: a prospective cohort study.Frontiers in oral health · 2024Article
- Stability of fiducial cephalometric landmarks of growing Class II malocclusion patients: a three-dimensional retrospective study.The Angle orthodontist · 2022Article
- Evaluation of an automated superimposition method based on multiple landmarks for growing patients.The Angle orthodontist · 2022Article
- Evaluation of automated cephalometric analysis based on the latest deep learning method.The Angle orthodontist · 2021Article
- How much deep learning is enough for automatic identification to be reliable?The Angle orthodontist · 2020Article
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Authors and funding
3 authors.
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Abstract
objectivesTo 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
methodsA 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.
resultsThe 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.
conclusionsThe proposed method that was compatible with computer-aided cephalometrics might be a reliable superimposition method for superimposing serial cephalometric images.
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