A quantitative approach for measuring crowding in the dental arch: Fourier descriptors

Am J Orthod Dentofacial Orthop. 2004 Jun;125(6):716-25. doi: 10.1016/j.ajodo.2003.05.008.

Abstract

Dental crowding is defined as a discrepancy between tooth size and jaw size that results in a misalignment of the tooth row. Proposed reasons for crowding include excessively large teeth, small jaws, and a combination of both. Nevertheless, the parameters that would allow the prediction of crowding have not been identified. This study compared the shape of crowded and uncrowded dental arches, matched for size and sex. The application of elliptical Fourier functions (EFFs) provided an accurate numeric description of the dental arch form. Dental casts from the Nihon University School of Dentistry at Matsudo, Chiba, Japan, were studied. Group I, the control group, consisted of 118 dental cast pairs (49 female, 69 male, aged 20.40 +/- 1.68 years [mean +/- SD]) with little or no crowding. Group II, which exhibited crowding, consisted of 78 dental cast pairs (64 female, 14 male, aged 19.67 +/- 4.95 years). From photographs, a set of 24 homologous points describing the tooth row was identified. These points were then fitted with EFFs. Each maxillary and mandibular outline was subsequently standardized for size by scaling the bounded area to a constant 10,000 mm(2). These "shape only" data were used to assess differences between arches in the 2 groups. By multivariate analysis of variance, statistically significant shape differences between groups I and II were obtained for both arches. Patients with crowding exhibited more variability than did the controls. This variability was illustrated with canonical axes derived from discriminant function analysis.

MeSH terms

  • Adolescent
  • Adult
  • Case-Control Studies
  • Computer Simulation
  • Dental Arch / anatomy & histology*
  • Dental Arch / pathology*
  • Discriminant Analysis
  • Female
  • Fourier Analysis
  • Humans
  • Male
  • Malocclusion / pathology*
  • Models, Biological
  • Multivariate Analysis
  • Sex Characteristics