Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism

• NCJ Number: 304551
• Journal: Scientific Reports Volume: 11 Issue: 1 Dated: 2021
• Author(s): H. S. Matthews; et al
• Date Published: 2021
• Grant Number(s): 2014-DN-BX-K031; 2018-DU-BX-0219
• Agencies: NIJ-Sponsored
• Award: Improving the prediction of human quantitative pigmentation traits such as eye, hair and skin color using a worldwide representation panel of US and European individuals; Ensuring standards and enhancing the field of predictive biometrics using a globally-diverse genotype-phenotype database

Annotation

In this article we derive and present open-source three-dimensional (3D) growth curves of the human face.

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. 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. (Publisher Abstract)