Prediction methods of maxillary canine impaction: a systematic review

• Published in: Acta odontologica Scandinavica Vol. 80; no. 1; p. 51
• Main Authors: Jiménez-Silva, Antonio, Carnevali-Arellano, Romano, Vivanco-Coke, Sheilah, Tobar-Reyes, Julio, Araya-Díaz, Pamela, Palomino-Montenegro, Hernán
• Format: Journal Article
• Language: English
• Published: England 02-01-2022
• Subjects: Cuspid; Humans; Radiography, Panoramic; Tooth, Impacted - diagnostic imaging; early diagnosis; predictors; systematic review; Maxillary canine impaction
• ISSN: 1502-3850
• DOI: 10.1080/00016357.2021.1936163

To describe and assess the available evidence of prediction methods of maxillary canine impaction (MCI).
A systematic search was conducted through PubMed, Cochrane Library, Embase, EBSCOhost, Scopus, ScienceDirect, Bireme and Scielo until December 2020. This systematic review was conducted according to the PRISMA statement. The methodology of the selected studies was assessed using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2).
A total of 2391 articles were identified in the first approach and after a 2-phase selection, 11 studies were included in qualitative analysis. Prediction methods were constructed using equation-based models, geometric measurements and computational methods from clinical and imaging data to predict palatal/buccal MCI. The quality of evidence was low to moderate due to the presence of risk of bias in most of the studies included. Three cohort studies with the best methodological quality proposed prediction models based on geometric measurements, canine position and facial growth pattern that would allow predicting MCI from CBCT, lateral and panoramic radiographs.
The evidence is limited and most of the studies present a low methodological quality. However, it is possible to suggest that some prediction methods based on the position of the canine and facial growth pattern could predict palatal/buccal MCI in mixed dentition. Cohort studies with better methodological quality and long-term follow-up are needed to better validate a prediction model.