A geometric morphometric study on variations in the hard and soft tissue auditory matrices of South African populations

Abstract

The ear is a complex structure that has been mostly ignored in facial approximation. Thus, information is limited on how to reliably estimate its shape for craniofacial approximations. Current manual facial approximation methods employed in South Africa use standard ear casts, with no consideration of the influence of sex, age, and ancestry on the morphological structure of the ear. As the field of facial approximation moves towards automated and computerised methods, more studies are being aimed at developing sex-, age-, and ancestry-specific databases. This study aims to assess variations and associations in ear shape and the eyes, nose, and mouth; and the underlying hard tissues of the external auditory meatus (EAM), nasal bones, nasal aperture, orbits, zygoma, and maxilla, in a sample of 40 black South Africans and 76 white South Africans between the ages of 18 and 90 years of age. A total of 50 capulometric and 43 craniometric landmarks were automatically placed on a sample of Cone Beam Computed Tomography (CBCT) scan reconstructions using MeVisLab 2.7.1. A further 559 semi-landmarks were automatically placed along the curves of the EAM, orbits, and anterior nasal aperture. The cartesian coordinates were recorded and analysed using geometric morphometric methods (GMM). General Procrustes Analysis (GPA), Principal Component Analysis (PCA), and multivariate normality testing were performed on all hard and soft tissue matrices for the entire sample, and each ancestral group separately. Both hard- and soft-tissue auditory matrices resulted in statistically significant asymmetry (p-value = 0.007). Thus, left and right matrices for the ears and EAM were assessed individually. Statistical analysis performed using MANOVA, revealed highly significant variation in ear shape between groups for ancestry (p-value = 0.001), while sex and age were only significant between the white South African sub-sample (p -value < 0.05). The influence of ancestry in EAM shape was also found to be highly significant (p-value = 0.001), with sex only significantly influencing the right EAM and age not being significant. Size was only found to significantly influence shape on the auditory hard-tissues and not the soft-tissue ear. Strong positive correlations were observed between the soft-tissue ear and EAM (r2 > 0.7). The ear was also tested for correlations against other facial features, with strong positive correlations observed between shapes of the ear and orbit, mid-facial matrix, and nose – iv which is the facial feature most cited in the literature as the base of understanding for estimating the size and shape of the ear. The results of this study support the use of automatic landmarking procedures to collect data on large samples, and the accurate placement of sliding landmarks will allow a better understanding of the shape of curves. Variations between groups indicate a need for population-specific databases for estimating the shape of the ear in South Africa. When estimating the shape of the ear, other facial features should be considered and factors such as the influence of ancestry included in the approximation. Sex and age will be of lesser concern when creating predictive models, as well as the influence of allometry.