Radiographic predictors for maxillary canine impaction

Abstract

Introduction: The aims of this study were to compare 3-dimensional cone-beam computed tomography (CBCT) images of patients with unilaterally impacted canines and to determine the possible radiographic factors for prediction of maxillary canine impactions. Methods: The sample consisted of 65 patients ranging in age from 9.6 to 13.8 years. The patients comprised 43 girls and 22 boys, with a mean age of 12.1 years and a median age of 12.2 years (+/- SD 1.23 years). Of the impacted maxillary canines, 32 were located on the right side and 33 on the left side. CBCT radiography was available for all patients. The diagnosis of a unilaterally impacted canine was determined from the patients' dental records as a unilateral failure of the canine to erupt at its appropriate site in the dental arch in contrast to normal eruption of the contralateral side. Radiographic follow-up assessment to identify unilateral impaction was performed for 1 year. The radiographic variables generated and the specific features investigated were collected with 3-dimensional CBCT imaging and compared between the impacted and the contralateral maxillary canines. Results: Statistically significant differences were found between the impacted and nonimpacted canines. Based on these results, clinical relevance and correlations among the variables- canine rotation, canine crown position, canine cusp tip to the midline and to the occlusal plane, canine angle to the midline, and canine angle to the lateral incisor-were considered as variables in a multivariable prediction model. A prediction model using CBCT for canine impaction was established (area under the curve, 0.965; 95% confidence interval, 0.936 to 0.995). Canine crown position, canine cusp tip to the occlusal plane, and canine angulation to the lateral incisor were the relevant predictors in this model. Conclusions: Prediction of canine impaction based on CBCT was excellent. The probability of canine impaction obtained from the prediction model can help orthodontists to define the optimal intervention method.