Browsing by Author "Matthews, Harold S."

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    Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism
    (Springer Nature, 2021-06-09) Matthews, Harold S.; Palmer, Richard L.; Baynam, Gareth S.; Quarrell, Oliver W.; Klein, Ophir D.; Spritz, Richard A.; Hennekam, Raoul C.; Walsh, Susan; Shriver, Mark; Weinberg, Seth M.; Hallgrimsson, Benedikt; Hammond, Peter; Penington, Anthony J.; Peeters, Hilde; Claes, Peter D.; Biology, School of Science
    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.
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    Using data‐driven phenotyping to investigate the impact of sex on 3D human facial surface morphology
    (Wiley, 2023) Matthews, Harold S.; Mahdi, Soha; Penington, Anthony J.; Marazita, Mary L.; Shaffer, John R.; Walsh, Susan; Shriver, Mark D.; Claes, Peter; Weinberg, Seth M.; Biology, School of Science
    The effects of sex on human facial morphology have been widely documented. Because sexual dimorphism is relevant to a variety of scientific and applied disciplines, it is imperative to have a complete and accurate account of how and where male and female faces differ. We apply a comprehensive facial phenotyping strategy to a large set of existing 3D facial surface images. We investigate facial sexual dimorphism in terms of size, shape, and shape variance. We also assess the ability to correctly assign sex based on shape, both for the whole face and for subregions. We applied a predefined data-driven segmentation to partition the 3D facial surfaces of 2446 adults into 63 hierarchically linked regions, ranging from global (whole face) to highly localized subparts. Each facial region was then analyzed with spatially dense geometric morphometrics. To describe the major modes of shape variation, principal components analysis was applied to the Procrustes aligned 3D points comprising each of the 63 facial regions. Both nonparametric and permutation-based statistics were then used to quantify the facial size and shape differences and visualizations were generated. Males were significantly larger than females for all 63 facial regions. Statistically significant sex differences in shape were also seen in all regions and the effects tended to be more pronounced for the upper lip and forehead, with more subtle changes emerging as the facial regions became more granular. Males also showed greater levels of shape variance, with the largest effect observed for the central forehead. Classification accuracy was highest for the full face (97%), while most facial regions showed an accuracy of 75% or greater. In summary, sex differences in both size and shape were present across every part of the face. By breaking the face into subparts, some shape differences emerged that were not apparent when analyzing the face as a whole. The increase in facial shape variance suggests possible evolutionary origins and may offer insights for understanding congenital facial malformations. Our classification results indicate that a high degree of accuracy is possible with only parts of the face, which may have implications for biometrics applications.