A new guide using CBCT to identify the severity of maxillary canine impaction and predict the best method of intervention

OBJECTIVE: This study aimed to test the intra and inter-rater reliability, reproducibility, and validity of a new guide called the “Cone Beam Computed Tomography-Maxillary Canine Impaction (CBCT-MCI),” designed to assess the position, severity, and predictability of the maxillary canine impaction (MCI) treatment intervention using Cone Beam Computed Tomography (CBCT). MATERIALS AND METHODS: This study is a retrospective radiographic review of 44 patients with unilateral or bilateral MCI. A total of 56 maxillary impacted canines’ CBCT radiographs were analyzed using the new CBCT-MCI guide, with each of its 10 items scored. The total scores of the 10 items were then compared to a scale to predict the best treatment intervention of MCI: normal spontaneous eruption, surgical-orthodontic traction, or surgical extraction. Radiologists and orthodontists have developed, tested, and retested the CBCT-MCI guide on the same 56 maxillary impacted canines using CBCT radiographs to check its reliability and reproducibility using the Kappa coefficient. Furthermore, the validity of this guide was tested by comparing the predicted best treatment intervention with the actual treatment administered to the assessed impacted maxillary canine using the Kappa coefficient and percentage of agreement using cross-tabulation. RESULTS: The result of this study showed significantly strong Kappa values of intra-rater agreement (k = 0.91, (P < 0.0001) and inter-rater agreement (k = 0.84, P < 0.0001). Furthermore, testing the relationship between the two MCI treatment interventions using cross-tabulation, the agreement percentages between the predicated and actual treatment plans of the assessed MCI ranged between 70% and 95.5% with a significantly strong Kappa value (k = 0.82, P < 0.0001). CONCLUSIONS: This study suggests that the CBCT-MCI guide is capable of producing accurate, reliable, and reproducible results in assessing and predicting the type of orthodontic treatment intervention of MCI in a simple, quick, and efficient way.