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Testing regression and mean model approaches to facial soft-tissue thickness estimation
Tobias Mr Houlton1, Nicolene Jooste2, Maryna Steyn1
1Human Variation and Identification Research Unit (HVIRU), School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Parktown, South Africa.
Medicine, Science, and the Law
|November 30, 2020
Summary
This study developed a subject-specific regression model to estimate facial soft-tissue thickness (FSTT) in South Africans. The new model offers more accurate FSTT estimations for craniofacial identification than existing methods.
Area of Science:
- Forensic Anthropology
- Craniofacial Identification
- Medical Imaging
Background:
- Facial soft-tissue thickness (FSTT) data are crucial for craniofacial identification.
- Existing FSTT databanks often rely on average values, potentially limiting accuracy.
- Subject-specific estimation methods are needed for improved forensic applications.
Purpose of the Study:
- To develop and validate a subject-specific regression model for estimating FSTT at oral midline landmarks.
- To compare the accuracy of the new model against existing FSTT estimation methods.
- To assess the utility of skeletal projection measurements in FSTT estimation.
Main Methods:
- Cone-beam computed tomography (CBCT) scans of 100 South African individuals were analyzed.
- Subject-specific regression equations were generated using skeletal projection measurements, incorporating sex categories.
- Validation involved comparing the new model's FSTT estimations with various mean FSTT data and existing regression models.
Main Results:
- The generated regression equations significantly improved the goodness-of-fit (r²-value).
- The new subject-specific model demonstrated a low total mean inaccuracy (TMI) of 1.53 mm (dental) and 1.55 mm (CEJ).
- The devised regressions outperformed most tested mean models and substantially outperformed a pre-existing regression model.
Conclusions:
- Subject-specific regression models provide a more accurate approach to FSTT estimation in a South African population.
- The developed model using skeletal projection measurements offers a promising tool for craniofacial identification.
- Further research with larger sample sizes and validation is recommended to support broader application.
