RESUMEN
This study aimed to determine the possible association between disc displacement (DD) disorders and malocclusion complexity. This cross-sectional study was carried out using a case-control design. The Research Diagnosis Criteria for Temporomandibular Disorders were used to identify cases and controls. The Index of Complexity, Outcome, and Need (ICON) was used to quantify malocclusion complexity as easy, mild, moderate, difficult, or very difficult. A total of 310 subjects were included: 130 cases and 180 controls. A binary logistic regression (p < 0.05) was used to identify associations. The odds ratio (OR) was also calculated. DD was associated with sex, age, and malocclusion complexity (p < 0.05). The malocclusion complexity comparison showed that 89.3% of the controls fell within the easy-moderate levels of the ICON, whereas 85.4% of the cases were in the moderate-very difficult levels (p ≤ 0.001). Difficult and very difficult malocclusions had high ORs (9.801 and 9.689, respectively) compared to the easy cases. In conclusion, patients with malocclusion complexity levels classified as difficult or very difficult have greater odds of presenting DD.
RESUMEN
Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.