RESUMO
As an alternative to the conventional fixed appliance that uses orthodontic brackets, a simple round tube without a bonding base can be bonded to the tooth surface by covering the tube with flowable resin. In this technique, bent wires cannot be inserted into the simple tubes; therefore, repositioning of the simple tubes is often required for adjustments. To reduce repositioning of simple tubes, a dome-shaped resin covering of the simple tube can be designed with a customized in-and-out compensation, using three-dimensional computer-aided design software based on digital simulation of orthodontic tooth movement. In the present case, the use of simple tubes bonded with customized resin coverings in a Class I nonextraction case is described in a 17-year-old male, in whom moderate crowding of the anterior teeth was treated over an 8-month period. This case shows that simple tubes can be used as an alternative to brackets in some Class I nonextraction cases, with the potential benefit of reducing decalcification.
RESUMO
Understanding the classification of malocclusion is a crucial issue in Orthodontics. It can also help us to diagnose, treat, and understand malocclusion to establish a standard for definite class of patients. Principal component analysis (PCA) and k-means algorithms have been emerging as data analytic methods for cephalometric measurements, due to their intuitive concepts and application potentials. This study analyzed the macro- and meso-scale classification structure and feature basis vectors of 1020 (415 male, 605 female; mean age, 25 years) orthodontic patients using statistical preprocessing, PCA, random matrix theory (RMT) and k-means algorithms. RMT results show that 7 principal components (PCs) are significant standard in the extraction of features. Using k-means algorithms, 3 and 6 clusters were identified and the axes of PC1~3 were determined to be significant for patient classification. Macro-scale classification denotes skeletal Class I, II, III and PC1 means anteroposterior discrepancy of the maxilla and mandible and mandibular position. PC2 and PC3 means vertical pattern and maxillary position respectively; they played significant roles in the meso-scale classification. In conclusion, the typical patient profile (TPP) of each class showed that the data-based classification corresponds with the clinical classification of orthodontic patients. This data-based study can provide insight into the development of new diagnostic classifications.