Int J Oral Biol.
2018 Jun;43(2):101-111. 10.11620/IJOB.2018.43.2.101.
Multiscale Clustering and Profile Visualization of Malocclusion in Korean Orthodontic Patients: Cluster Analysis of Malocclusion
- Affiliations
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- 1Department of Orthodontics, School of Dentistry, Chosun University, Gwangju, Korea. shlim@chosun.ac.kr
- 2Natural Science Research Institute, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
- 3Korea electronic power corporation, Naju, Jeollanam-do, Korea.
- 4Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
- KMID: 2437361
- DOI: http://doi.org/10.11620/IJOB.2018.43.2.101
Abstract
- 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.
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