Nose approximation among South African groups from cone-beam computed tomography (CBCT) using a new computer-assisted method based on automatic landmarking.

Considering the high demand for the identification of unknown remains in South Africa, a need exists to establish reliable facial approximation techniques that will take into account sex and age and, most importantly, be useful within the South African context. This study aimed to provide accurate statistical models for predicting nasal soft-tissue shape from information about the underlying skull subtract among a South African sample. The database containing 200 cone-beam computer tomography (CBCT) scans (100 black South Africans and 100 white South Africans). The acquisition and extraction of the 3D relevant anatomical structures (hard- and soft-tissue) were performed by an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using MeVisLab © v. 2.7.1 software. An evaluation of shape differences attributed to known factors (ancestry, sex, size, and age) was performed using geometric morphometric and statistical models of prediction were created using a Projection onto Latent Structures Regression (PLSR) algorithm. The accuracy of the estimated soft-tissue nose was evaluated in terms of metric deviations on training and un-trained datasets. Our findings demonstrated the influence of factors (sex, aging, and allometry) on the variability of the hard- and soft-tissue among two South African population groups. This research provides accurate statistical models optimized by including additional information such as ancestry, sex, and age. When using the landmark-to landmark distances, the prediction errors ranged between 1.769mm and 2.164mm for black South Africans at the tip of the nose and the alae, while they ranged from 2.068mm to 2.175mm for the white subsample. The prediction errors on un-trained data were slightly larger, ranging between 2.139mm and 2.833mm for the black South African sample at the tip of the nose and the alae and ranging from 2.575mm to 2.859mm for the white South African sample. This research demonstrates the utilization of an automated 3Dmethod based on an automatic landmarking method as a convenient prerequisite for providing a valid and reliable nose prediction model that meets population-specific standards for South Africans.status: publishe

Automatic landmarking as a convenient prerequisite for geometric morphometrics. Validation on cone beam computed tomography (CBCT)- based shape analysis of the nasal complex.

Manual landmarking is used in several manual and semi-automated prediction guidelines for approximation of the nose. The manual placement of landmarks may, however, render the analysis less repeatable due to observer subjectivity and, consequently, have an impact on the accuracy of the human facial approximation. In order to address this subjectivity and thereby improve facial approximations, we are developing an automated three-dimensional (3D) method based on an automatic dense landmarking procedure using non-rigid surface registration. The aim of this study was to validate the automatic landmarking method by comparing the intra-observer errors (INTRA-OE) and inter-observer errors (INTER-OE) between automatic and manual landmarking. Cone beam computed tomography (CBCT) scans of adult South Africans were selected from the Oral and Dental Hospital, University of Pretoria, South Africa. In this study, the validation of the automatic landmarking was performed on 20 3D surfaces. INTRA-OE and INTER-OE were analyzed by registering 41 craniometric landmarks from 10 hard-tissue surfaces and 21 capulometric landmarks from 10 soft-tissue surfaces of the same individuals. Absolute precision of the landmark positioning (both on the samples as well as the template) was assessed by calculating the measurement error (ME) for each landmark over different observers. Systematic error (bias) and relative random error (precision) was further quantified through repeated measures ANOVA (ANOVA-RM). The analysis showed that the random component of the ME in landmark positioning between the automatic observations were on average on par with the manual observations, except for the soft-tissue landmarks where automatic landmarking showed lower ME compared to manual landmarking. No bias was observed within the craniometric landmarking methods, but some bias was observed for capulometric landmarking. In conclusion, this research provides a first validation of the precision and accuracy of the automatic placement of landmarks on 3D hard- and soft-tissue surfaces and demonstrates its utilization as a convenient prerequisite for geometric morphometrics based shape analysis of the nasal complex.status: publishe