Dr. Ankoniya Amoria
With the current widespread use of three-dimensional (3D) facial surface imaging in clinical and research environments, there is a growing demand for high-quality craniofacial norms based on 3D imaging technology. The principal goal of the 3D Facial Norms (3DFN) project was to create an interactive, Web-based repository of 3D facial images and measurements. Unlike other repositories, users can gain access to both summary-level statistics and individual-level data, including 3D facial landmark coordinates, 3D-derived anthropometric measurements, 3D facial surface images, and genotypes from every individual in the dataset. The 3DFN database currently consists of 2454 male and female participants ranging in age from 3 to 40 years. The subjects were recruited at four US sites and screened for a history of craniofacial conditions. The goal of this article is to introduce readers to the 3DFN repository by providing a general overview of the project, explaining the rationale behind the creation of the database, and describing the methods used to collect the data. Sex- and age-specific summary statistics (means and standard deviations) and growth curves for every anthropometric measurement in the 3DFN dataset are provided as a supplement available online. These summary statistics and growth curves can aid clinicians in the assessment of craniofacial dysmorphology.
The success of investigations into the underlying causes and effective treatment of craniofacial malformations depends on the acquisition of objective, reliable, and carefully collected data on the craniofacial phenotype. Many individuals with congenital anomalies that affect the head and face present with subtle morphologic disturbances. Implicit in any description of facial dysmorphology is the notion that the phenomenon under consideration represents a deviation from some “normal” or baseline state. Thus, all descriptions of dysmorphology are inherently comparative by nature. As a consequence, an understanding of what constitutes the range of normal variation for craniofacial features is essential. Although attempts to quantify the human face date back to antiquity, standardized methods for measuring the human face were only developed in the early 20th century. In response to the clinical community's need for populationbased norms, large datasets comprising standardized facial anthropometric or cephalometric measures were eventually constructed. In order to fully capture the variation present in the general population while simultaneously providing age-, sex-, and ethnicity-specific normative data, large numbers of healthy individuals were required for these databases. Clinicians and researchers routinely use these normative datasets to determine how measurements from a particular patient or subject compare with those of their peers and to perform group-based morphologic comparisons. When quantitative measurements are combined with genomic information within a single craniofacial database, the possibility of mapping the genes that underlie normal variation in craniofacial traits becomes possible.
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