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Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data.

ABSTRACT: Modelling of the development of facial morphology during childhood and adolescence is highly useful in forensic and biomedical practice. However, most studies in this area fail to capture the essence of the face as a three-dimensional structure. The main aims of our present study were (1) to construct ageing trajectories for the female and male face between 7 and 17 years of age and (2) to propose a three-dimensional age progression (age -regression) system focused on real growth-related facial changes. Our approach was based on an assessment of a total of 522 three-dimensional (3D) facial scans of Czech children (39 boys, 48 girls) that were longitudinally studied between the ages of 7 to 12 and 12 to 17 years. Facial surface scans were obtained using a Vectra-3D scanner and evaluated using geometric morphometric methods (CPD-DCA, PCA, Hotelling's T2 tests). We observed very similar growth rates between 7 and 10 years in both sexes, followed by an increase in growth velocity in both sexes, with maxima between 11 and 12 years in girls and 11 to 13 years in boys, which are connected with the different timing of the onset of puberty. Based on these partly different ageing trajectories for girls and boys, we simulated the effects of age progression (age regression) on facial scans. In girls, the mean error was 1.81 mm at 12 years and 1.7 mm at 17 years. In boys, the prediction system was slightly less successful: 2.0 mm at 12 years and 1.94 mm at 17 years. The areas with the greatest deviations between predicted and real facial morphology were not important for facial recognition. Changes of body mass index percentiles in children throughout the observation period had no significant influence on the accuracy of the age progression models for both sexes.

SUBMITTER: Koudelova J

PROVIDER: S-EPMC6386244 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data.

Koudelová Jana J Hoffmannová Eva E Dupej Ján J Velemínská Jana J

PloS one 20190222 2

Modelling of the development of facial morphology during childhood and adolescence is highly useful in forensic and biomedical practice. However, most studies in this area fail to capture the essence of the face as a three-dimensional structure. The main aims of our present study were (1) to construct ageing trajectories for the female and male face between 7 and 17 years of age and (2) to propose a three-dimensional age progression (age -regression) system focused on real growth-related facial ...[more]

PMID: 30794623

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Project description:Aneurysm clipping requires the proficiency of several skills, yet the traditional way of practicing them has been recently challenged. The use of simulators could be an alternative educational tool. The aim of this data analysis is to provide further evaluation of a reusable low-cost 3D printed training model we developed for aneurysm clipping [1]. The simulator was designed to replicate the bone structure, arteries and targeted aneurysms. Thirty-two neurosurgery residents performed a craniotomy and aneurysm clipping using the model and then filled out a survey. The survey was designed in two parts: a 5-point Likert scale questionnaire and three questions requiring written responses [1]. Two dimensions of the model were evaluated by the questionnaire: the face validity, assessed by 5 questions about the realism of the model, and the content validity, assessed by 6 questions regarding the usefulness of the model during the different steps of the training procedure. The three questions requiring written responses referred to the strengths and weaknesses of the simulator and a global yes/no question as to whether or not they would repeat the experience. Demographic data, experience level and survey responses of the residents were grouped in a dataset [2]. A descriptive analysis was performed for each dimension. Then, the groups were compared according to their level of expertise (Junior and Senior groups) with an independent sample t-test. A Confirmatory Factor Analysis (CFA) was estimated, using a Weighted Least Squares Mean Variance adjusted (WLSMV) which works best for the ordinal data [3]. Fitness was calculated using chi-square (χ2) test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and the Root Mean Square Error of Approximation (RMSEA). A non-significant χ2, CFI and TLI greater than 0.90 and RMSEA < 0.08 were considered an acceptable fit [4]. All data analysis was performed using IBM SPSS 23.0 statistical software. Data are reported as mean + standard deviation (SD). A probability p < 0.05 was considered significant. Exploratory Factor Analysis was done to explore the factorial structure of the 11-items scale in the sample, first we performed a principal components analysis. The Kaiser-Meyer-Olkin measure verified the sampling adequacy for the analysis (KMO = 0.784; Bartlett's Test of Sphericity χ2 (55) = 243.44, p < .001), indicating correlation is adequate for factor analysis. Considering Eigen values greater than 1, a two-factor solution explained 73.1% of the variance but left one item in factor 2 (Q 11). The results of this factor analysis are presented in Table 1. Confirmatory Factor Analysis, considering only the 10 items in the first factor (removing question 11 of our model), was performed. This model reached the following fit: χ2 (35) = 38.821, p > .05; CFI = 0.997; TLI = 0.996; RMSEA 0.058, without any error terms to exhibit covariance. Regarding the reliability of the questionnaire, the internal consistency was explored in the 10 items selected in the confirmatory factor analysis with an alpha coefficient (α = 0.941).

Dataset Information

Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data.

Publications

Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data.

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