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Sagittal, Vertical, and Transverse Skeletal Characteristics in Individuals with Impacted Maxillary Canines: A
Nuri Can Tanrısever1, Mehmet Okan Akçam1
1Department of Orthodontics, School of Dentistry, University of Ankara, 06560 Ankara, Türkiye.
None:
Background/Objectives: Maxillary canine impaction is a multifactorial condition that may adversely affect esthetics, function, and occlusal stability. Although various etiologic factors have been proposed, the skeletal characteristics observed in individuals with maxillary canine impaction remain incompletely understood. This study aimed to evaluate sagittal, vertical, and transverse skeletal characteristics in individuals with impacted maxillary canines using lateral and posteroanterior cephalometric radiographs. Methods: This retrospective study included lateral and posteroanterior cephalometric radiographs of 100 individuals (mean age: 15.85 ± 1.69 years) with at least one impacted maxillary canine. Sagittal skeletal relationships were assessed using SNA, ANB, and Wits appraisal, while vertical relationships were evaluated using the GoGn/SN and FH/MP angles. Transverse skeletal dimensions (JR-JL, JR-ZAg, JL-ZAg, and Ag-Ag) were evaluated and compared with a matched control group without impacted maxillary canines using independent-samples t-tests. Results: Sagittal and vertical evaluations demonstrated distributional patterns of skeletal classifications within the impaction sample. Transverse analysis revealed significantly reduced maxillary width (JR-JL) and increased maxillomandibular transverse measurements (JR-ZAg and JL-ZAg) in the impaction group compared with the control group (p < 0.001). Conclusions: Sagittal and vertical skeletal findings represent distributional characteristics among affected individuals, whereas statistically significant differences were identified only for transverse skeletal dimensions. These findings underscore the clinical relevance of transverse skeletal assessment in individuals with impacted maxillary canines. Prospective studies are required before such observations can be translated into risk prediction.
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