Testing regression and mean model approaches to facial soft-tissue thickness estimation

Tobias M. R. Houlton (Lead / Corresponding author), Nicolene Jooste, Maryna Steyn

Research output: Contribution to journalArticlepeer-review

Abstract

Average facial soft-tissue thickness (FSTT) databanks are continuously developed and applied within craniofacial identification. This study considered and tested a subject-specific regression model alternative for estimating the FSTT values for oral midline landmarks using skeletal projection measurements. Measurements were taken from cone-beam computed tomography scans of 100 South African individuals (60 male, 40 female; Mage = 35 years). Regression equations incorporating sex categories were generated. This significantly improved the goodness-of-fit (r2-value). Validation tests compared the constructed regression models with mean FSTT data collected from this study, existing South African FSTT data, a universal total weighted mean approach with pooled demographic data and collection techniques and a regression model approach that uses bizygomatic width and maximum cranial breadth dimensions. The generated regression equations demonstrated individualised results, presenting a total mean inaccuracy (TMI) of 1.53 mm using dental projection measurements and 1.55 mm using cemento-enamel junction projection measurements. These slightly outperformed most tested mean models (TMI ranged from 1.42 to 4.43 mm), and substantially outperformed the pre-existing regression model approach (TMI = 5.12 mm). The newly devised regressions offer a subject-specific solution to FSTT estimation within a South African population. A continued development in sample size and validation testing may help substantiate its application within craniofacial identification.

Original languageEnglish
Pages (from-to)170-179
Number of pages10
JournalMedicine, Science and the Law
Volume61
Issue number3
Early online date29 Nov 2020
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • craniofacial approximation
  • Craniofacial identification
  • craniofacial superimposition
  • mouth
  • soft-tissue depth
  • soft-tissue thickness

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