title: Brief communication: correcting overestimation when determining two-dimensional occlusal area in human molars.
abstract: The robustness index (RI) is determined by multiplying dental mesiodistal and buccolingual diameters, and is used to estimate occlusal area. However, because teeth are not rectangular its calculation consistently causes overestimations. Moreover, teeth, in particular molars, are not identically shaped so overestimations vary. The current study seeks to determine the extent to which overestimations are affected by tooth shape and to improve RI's efficacy. Initially, 120 molars were sorted into six shape groups, which were determined by hypocone/hypoconulid expression. Three maxillary and three mandibular shape groups were set using the Arizona State University Dental Anthropology System. ANOVA results determined that RI overestimations, which averaged around 20%, were not the same for each shape category. Maxillary molars with large hypocones and mandibular molars with no hypoconulids were overestimated significantly less than the other molar groups. Regression-based correction formulae were generated and applied to the original sample. These formulae far more precisely estimated tooth area than RI and there were no differences in estimation based upon tooth shape. A subsequent validation study of 24 additional molars was undertaken to test the formulae on teeth not from the original sample. Overestimation/underestimation averaged 0.5% and was about the same for each of the tooth shape groups. Finally, six new correction formulae were generated using all 144 molars. The correction formulae provide, what is termed here, an adjusted robustness index (ARI), and it is recommended that ARI is used in future studies of molar occlusal area.
authors.affiliation: Department of Anthropology, University of Indianapolis, Indianapolis, IN 46227, USA. cschmidt@uindy.edu
authors.forename: Christopher
authors.initials: C
authors.lastname: Schmidt
authors.forename: Stephen
authors.initials: S
authors.lastname: Ousley
authors.forename: Molly
authors.initials: M
authors.lastname: Schmidt
mesh_terms: D000704:Analysis of Variance; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008963:Molar / Q000033:anatomy & histology*