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Updated: Jun 22, 2026
Quantification of Orofacial Phenotypes in Xenopus
Published on: November 6, 2014
Classification and characterization of facial asymmetry in adult patients with skeletal Class III malocclusion using
Linlin Zhang1, Cong Cao1, Ting Ma1
1Department of Orthodontics, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University and Shandong Key Laboratory of Oral Tissue Regeneration and Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration and Shandong Provincial Clinical Research Center for Oral Diseases, Jinan, Shandong, China.
Introduction:
This study aimed to analyze the comprehensive maxillofacial features of patients with skeletal Class III malocclusion and facial asymmetry to develop a classification system for diagnosis and surgical planning.
Methods:
A total of 161 adult patients were included, with 121 patients in the asymmetry group (menton deviation >2 mm) and 40 patients in the symmetry group (menton deviation ≤2 mm). Twenty-eight variables were determined, including transverse translation, roll and yaw of each facial unit, transverse width, mandibular morphology, and transverse dental compensation. Principal component (PC) analysis was conducted to extract PCs, and cluster analysis was performed using these components to classify the asymmetry group. A decision tree was constructed on the basis of the clustering results.
Results:
Six PCs were extracted, explaining 80.622% of the data variability. The asymmetry group was classified into 4 subgroups: (1) atypical type (15.7%) showed an opposite roll direction of maxillary dentition than of menton deviation; (2) compound type (34.71%) demonstrated significant ramus height differences, maxillary roll, and mandibular roll and yaw; (3) mandibular yaw type (44.63%) showed slight mandibular yaw without mandibular morphology asymmetry; and (4) maxillary-shift type (4.96%) shared similarities with the compound type but showed significant maxillary translation. The classification and regression tree model achieved a prediction accuracy of up to 85.11%.
Conclusions:
This study identified 4 distinct phenotypes using cluster analysis and proposed tailored treatment recommendations on the basis of their specific characteristics. The classification results emphasized the importance of spatial displacement features, especially mandibular yaw, in diagnosing facial asymmetry. The established classification and regression tree model enables clinicians to identify patients conveniently.
