Morphological features of Class I, II and III malocclusions of Saudi adolescents.

AIMS: This cross-sectional study compared and contrasted the morphological characteristics of Class I, II and III malocclusions in an adolescent Saudi population. METHODS: Adolescent Saudis with Angle's Class I, II and III malocclusions were selected from orthodontic patients' records. Angular and linear measurements were compared between the three groups. Cephalometric analysis was performed using the VistadentOC® software. Multifactorial ANOVA for angular and linear measurements between and within groups. RESULTS: Orthodontic records of 300 patients were included. There was no significant difference between and within groups in age and distribution of Angle's classification, p > 0.05. Multifactorial ANOVA showed that there were significant interactions between gender and malocclusions in skeletal, dental and soft tissue measurements, p < 0.05. There were significant differences in the sagittal and vertical skeletal measurements between groups, p < 0.05. The dental measurements were also significantly different in most of the measurements (p < 0.05). Moreover, there were significantly different readings among the groups in the soft tissue analysis. CONCLUSION: Morphological characteristics of adolescent Saudis show unique differences between gender and malocclusions, more so in Class III malocclusions. Class II and III malocclusions also show skeletal differences amongst the groups.

Use of automated learning techniques for predicting mandibular morphology in skeletal class I, II and III.

BACKGROUND: The prediction of the mandibular bone morphology in facial reconstruction for forensic purposes is usually performed considering a straight profile corresponding to skeletal class I, with application of linear and parametric analysis which limit the search for relationships between mandibular and craniomaxillary variables. OBJECTIVE: To predict the mandibular morphology through craniomaxillary variables on lateral radiographs in patients with skeletal class I, II and III, using automated learning techniques, such as Artificial Neural Networks and Support Vector Regression. MATERIALS AND METHODS: 229 standardized lateral radiographs from Colombian patients of both sexes aged 18-25 years were collected. Coordinates of craniofacial landmarks were used to create mandibular and craniomaxillary variables. Mandibular measurements were selected to be predicted from 5 sets of craniomaxillary variables or input characteristics by using automated learning techniques, and they were evaluated through a correlation coefficient by a ridge regression between the real value and the predicted value. RESULTS: Coefficients from 0.84 until 0.99 were obtained with Artificial Neural Networks in the 17 mandibular measures, and two coefficients above 0.7 were obtained with the Support Vector Regression. CONCLUSION: The craniomaxillary variables used, showed a high predictability ability of the selected mandibular variables, this may be the key to facial reconstruction from specific craniomaxillary measures in the three skeletal classifications.