Growth Simulation of Facial/Head Model from Childhood to Adulthood

Lin, Alice J.; Lai, Shuhua; Cheng, Fuhua

doi:10.3722/cadaps.2010.777-786

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…This mode represents an overall increase in length of the face (and distance between the eyes) and a decrease in the distance between the endocanthion and pronasale. Broadly, one might interpret this as an elongation in facial shape, which is consistent with the growth of children [11]. Subtle differences are observed only between modes 1 at levels 1 (age) and 2 (all other variations) via mPCA, although we believe that these differences would become more apparent with increased number of landmark points.…”

Section: Resultssupporting

confidence: 61%

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Farnell

Galloway

Zhurov

et al. 2020

Communications in Computer and Information Science

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Here we study the effects of age on facial shape in adolescents by using a method called multilevel principal components analysis (mPCA). An associated multilevel multivariate probability distribution is derived and expressions for the (conditional) probability of age-group membership are presented. This formalism is explored via Monte Carlo (MC) simulated data in the first dataset; where age is taken to increase the overall scale of a three-dimensional facial shape represented by 21 landmark points and all other "subjective" variations are related to the width of the face. Eigenvalue plots make sense and modes of variation correctly identify these two main factors at appropriate levels of the mPCA model. Component scores for both single-level PCA and mPCA show a strong trend with age. Conditional probabilities are shown to predict membership by age group and the Pearson correlation coefficient between actual and predicted group membership is r = 0.99. The effects of outliers added to the MC training data are reduced by the use of robust covariance matrix estimation and robust averaging of matrices. These methods are applied to another dataset containing 12 GPA-scaled (3D) landmark points for 195 shapes from 27 white, male schoolchildren aged 11 to 16 years old. 21% of variation in the shapes for this dataset was accounted for by age. Mode 1 at level 1 (age) via mPCA appears to capture an elongation in facial shape with age, which is consistent with age-related shape changes in children. Component scores for both single-level PCA and mPCA again show a distinct trend with age. Conditional probabilities are again shown to reflect membership by age group and the Pearson correlation coefficient is given by r = 0.63 in this case. These analyses are an excellent first test of the ability of multilevel statistical methods to model age-related changes in facial shape in adolescents.

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Section: Resultssupporting

confidence: 61%

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Farnell

Galloway

Zhurov

et al. 2020

Communications in Computer and Information Science

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“…Multivariate modelling techniques have also been used to analyse coordinate 6 data directly in order to understand age-related changes in facial size and shape. These methods have included: simple "centering" and / or averaging techniques of facial shape at each age followed by a simple parametric model of growth [45], multivariate or kernel regression [46,47], principal components analysis (PCA) based methods [48][49][50], clustering and discriminant function analysis [51], and autoregressive moving averaging methods [52].…”

Section: Introductionmentioning

confidence: 99%

“…There has been much previous research relating to the process of facial changes in adolescents with respect to age (see, e.g., Refs. [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][66][67][68][69][70]). In particular, a review of agerelated facial changes [40] points out the difficulty of predicting changes in specific subjects, noting, for example, that: "the adolescent growth spurt in the mandible occurs in less than 25% of the cases, but the presence, onset, duration, and magnitude of the pubertal growth spurt in facial dimensions cannot be accurately predicted for any single individual."…”

Section: Introductionmentioning

confidence: 99%

Multilevel principal components analysis of three-dimensional facial growth in adolescents

Farnell

Richmond

Galloway

et al. 2020

Computer Methods and Programs in Biomedicine

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…However, our objective was not only to thoroughly evaluate the facial morphology of patients with WBS, NS, and 22q11.2DS, but also to assess their development and model the trajectories of age‐related changes at defined ages within the age range of 3–18 years. Such analyses might prove useful for differentiating between phenotypically similar syndromes and thus facilitate exact diagnosis, and enable more accurate genotype–phenotype correlations (Hammond et al, ; Hammond & Suttie, ; Lin, Lai, & Cheng, ; Sforza et al, ; Tartaglia et al, ; Tassabehji, ; Yagi et al, ).…”

Section: Discussionmentioning

confidence: 99%

Modeling age‐specific facial development in Williams–Beuren‐, Noonan‐, and 22q11.2 deletion syndromes in cohorts of Czech patients aged 3–18 years: A cross‐sectional three‐dimensional geometric morphometry analysis of their facial gestalt

Čaplovičová

Moslerová

Dupej

et al. 2018

American J of Med Genetics Pt A

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Three-dimensional (3D) virtual facial models facilitate genotype-phenotype correlations and diagnostics in clinical dysmorphology. Within cross-sectional analysis of both genders we evaluated facial features in representative cohorts of Czech patients with Williams-Beuren-(WBS; 12 cases), Noonan-(NS; 14), and 22q11.2 deletion syndromes (22q11.2DS; 20) and compared their age-related developmental trajectories to 21 age, sex and ethnically matched controls in 3-18 years of age. Using geometric morphometry statistically significant differences in facial morphology were found in all cases compared to controls. The dysmorphic features observed in WBS were specific and manifested in majority of cases. During ontogenesis, dysmorphic features associated with increased facial convexity become more pronounced whereas other typical features remained relatively stable. Dysmorphic features observed in NS cases were mostly apparent during childhood and gradually diminished with age. Facial development had a similar progress as in controls, while there has been increased growth of patients' nose and chin in adulthood. Facial characteristics observed in 22q11.2DS, except for hypoplastic alae nasi, did not correspond with the standard description of its facial phenotype because of marked facial heterogeneity of this clinical entity. Because of the sensitivity of 3D facial morphometry we were able to reach statistical significance even in smaller retrospective patient cohorts, which proves its clinical utility within the routine setting. K E Y W O R D S 22q11.2 deletion syndrome, 3D facial gestalt analysis, 3D imaging, dysmorphology, facial shape and size, geometric morphometry, Noonan syndrome, ontogenetic trajectory, Williams-Beuren syndrome

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Growth Simulation of Facial/Head Model from Childhood to Adulthood

Cited by 8 publications

References 7 publications

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Multilevel Models of Age-Related Changes in Facial Shape in Adolescents

Multilevel principal components analysis of three-dimensional facial growth in adolescents

Modeling age‐specific facial development in Williams–Beuren‐, Noonan‐, and 22q11.2 deletion syndromes in cohorts of Czech patients aged 3–18 years: A cross‐sectional three‐dimensional geometric morphometry analysis of their facial gestalt

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