Abstract
Craniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Delineation of salient facial characteristics can guide clinicians towards a correct clinical diagnosis and understanding the pathogenesis of the disorder. Abnormal facial shape might require craniofacial surgical intervention, with the restoration of normal shape an important surgical outcome. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. However, these fail to capture the full complexity of facial shape. With the increasing availability of 3D photographs, methods of assessment that take advantage of the rich information contained in such images are needed. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face. These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. We demonstrate the use of these growth curves for assessing patients and show that they identify normal and abnormal facial morphology independent from age-specific facial features. 3D growth curves can facilitate use of state-of-the-art 3D facial shape assessment by the broader clinical and biomedical research community. This advance in phenotype description will support clinical diagnosis and the understanding of disease pathogenesis including genotype–phenotype relations.
Highlights
Craniofacial dysmorphism is associated with thousands of genetic and environmental disorders
The normative data used in this study are 5,443 images sourced from 4 collections from different centers: the Australian Head Examination and Assessment Database (AHEAD; N = 853) from the Royal Children’s Hospital, Melbourne, Australia; the online 3D Facial Norms database (3DFN; N = 1886; https://www.facebase.org/facial_norms/); collections from Indiana University–Purdue University Indianapolis (IUPUI; N = 730) and Pennsylvania State University (PSU; N = 1973)
As more institutions collect 3D images as part of routine clinical care, there is a need for methods of facial assessment that take full advantage of the information available in such images
Summary
Craniofacial dysmorphism is associated with thousands of genetic and environmental disorders. Facial anthropometric growth curves or standards of single inter-landmark measurements have traditionally supported assessments of normal and abnormal facial shape, for both clinical and research applications. In this article we derive and present open-source three-dimensional (3D) growth curves of the human face These are sequences of age and sex-specific expected 3D facial shapes and statistical models of the variation around the expected shape, derived from 5443 3D images. Normative facial anthropometric and cephalometric growth curves, or ‘standards’, are central in assessing deviation from normal facial shape and treatment o utcomes[1,2,3,4] These are tabulated means and standard deviations of anthropometric measurements or mean cephalometric tracings, allowing clinical observations and measurements to be calibrated against what is expected for a given ancestry, age and sex. In the remainder of this article we present the growth curves, and demonstrate their utility for assessment of individual patients
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