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KMID : 0361920220520020085
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Korean Journal of Orthodontics
2022 Volume.52 No. 2 p.85 ~ p.101
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Characterization of facial asymmetry phenotypes in adult patients with skeletal Class III malocclusion using three-dimensional computed tomography and cluster analysis
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Ha Sang-Woon
Kim Su-Jung
Choi Jin-Young
Baek Seung-Hak
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Abstract
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Objective: To classify facial asymmetry (FA) phenotypes in adult patients with skeletal Class III (C-III) malocclusion.
Methods: A total of 120 C-III patients who underwent orthognathic surgery (OGS) and whose three-dimensional computed tomography images were taken one month prior to OGS were evaluated. Thirty hard tissue landmarks were identified. After measurement of 22 variables, including cant (°, mm), shift (mm), and yaw (°) of the maxilla, maxillary dentition (Max-dent), mandibular dentition, mandible, and mandibular border (Man-border) and differences in the frontal ramus angle (FRA, °) and ramus height (RH, mm), K-means cluster analysis was conducted using three variables (cant in the Max-dent [mm] and shift [mm] and yaw [°] in the Man-border). Statistical analyses were conducted to characterize the differences in the FA variables among the clusters.
Results: The FA phenotypes were classified into five types: 1) non-asymmetry type (35.8%); 2) maxillary-cant type (14.2%; severe cant of the Max-dent, mild shift of the Man-border); 3) mandibular-shift and yaw type (16.7%; moderate shift and yaw of the Man-border, mild RH-difference); 4) complex type (9.2%; severe cant of the Max-dent, moderate cant, severe shift, and severe yaw of the Man-border, moderate differences in FRA and RH); and 5) maxillary reverse-cant type (24.2%; reverse-cant of the Max-dent). Strategic decompensation by pre-surgical orthodontic treatment and considerations for OGS planning were proposed according to the FA phenotypes.
Conclusions: This FA phenotype classification may be an effective tool for differential diagnosis and surgical planning for Class III patients with FA.
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KEYWORD
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Facial asymmetry, Class III malocclusion, Cluster analysis
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