PubMed · 5287665
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R Y Norton. 1968. Malocclusion Class 2.. https://pubmed.ncbi.nlm.nih.gov/5287665/
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The purpose of this study was to determine whether the morphology of the midface differed in subjects with a retrognathic midfacial appearance (Class III malocclusions) using a combination of morphometric and cephalometric analyses. After obtaining appropriate consent, lateral cephalographs of 133 children of European-American descent, ages 5-11 years, were compared: 73 had Class III malocclusion, 60 had normal (Class I) occlusion. The cephalographs were traced and subdivided into seven age- and sex-matched groups. Average geometries based upon seven nodes (pterygoid point, PTS; rhinion, RO; posterior nasal spine, PNS; midpalatal point, MPP; anterior nasal spine, ANS; subspinale, A; prosthion, Pr), scaled to an equivalent size, were compared using a Procrustes routine. Euclidean distance matrix analysis (EDMA) was employed to localize differences in morphology. Bivariate analyses on unscaled data utilizing nine linear and six angular measurements were also undertaken. Results from Procrustes and EDMA analyses indicated that although the overall midfacial configurations differed statistically (P < 0.05), only about half of the seven age sub-groups maintained significance. Similarly, only four of the nine linear measures (PNS-MPP, MPP-ANS, A-Pr and PTS-RO) and two of the six angular parameters (PTS-RO-ANS and ANS-A-Pr) tested were significantly different (P < 0.05). Therefore, midfacial morphometric variability and morphological diversity may mask statistical differences. It is concluded that the midface may be the defining craniofacial component in the final appearance of Class III malocclusions compared to other craniofacial components, including the cranial base and mandible.
The computer-aided method presented is able to interpret and measure radiological structures automatically and reproducibly on a large number of randomly selected lateral head films of different film quality. For example the hard and soft tissue expression of the profile is used to discuss method, application and disadvantages. The performance of the system depends on image quality. The algorithm cannot be influenced by opacifications due to metal structure. In 90% of all cases the contours are identified correctly. Even if the image quality is poor, the recognition rate is about 84%. The selected cephalometric landmarks are placed in the right way in 85% of all cases. The constraint mathematical conditions and the high reproducibility improve the quality process of the cephalometric analysis. Recognition rates of 84% to 90% justify even nowadays the routine use of semi-automatic systems in PC-based analysis. With advancing digital radiography and improved computer performance, image interpreting systems will certainly become established.