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

dc.contributor.authorMatthews, Harold S.
dc.contributor.authorPalmer, Richard L.
dc.contributor.authorBaynam, Gareth S.
dc.contributor.authorQuarrell, Oliver W.
dc.contributor.authorKlein, Ophir D.
dc.contributor.authorSpritz, Richard A.
dc.contributor.authorHennekam, Raoul C.
dc.contributor.authorWalsh, Susan
dc.contributor.authorShriver, Mark
dc.contributor.authorWeinberg, Seth M.
dc.contributor.authorHallgrimsson, Benedikt
dc.contributor.authorHammond, Peter
dc.contributor.authorPenington, Anthony J.
dc.contributor.authorPeeters, Hilde
dc.contributor.authorClaes, Peter D.
dc.contributor.departmentBiology, School of Scienceen_US
dc.date.accessioned2022-12-09T16:22:58Z
dc.date.available2022-12-09T16:22:58Z
dc.date.issued2021-06-09
dc.description.abstractCraniofacial 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.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationMatthews HS, Palmer RL, Baynam GS, et al. Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism. Sci Rep. 2021;11(1):12175. Published 2021 Jun 9. doi:10.1038/s41598-021-91465-zen_US
dc.identifier.urihttps://hdl.handle.net/1805/30710
dc.language.isoen_USen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1038/s41598-021-91465-zen_US
dc.relation.journalScientific Reportsen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0*
dc.sourcePMCen_US
dc.subjectDiagnostic markersen_US
dc.subjectBiomedical engineeringen_US
dc.subjectStatistical methodsen_US
dc.subjectSoftwareen_US
dc.titleLarge-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphismen_US
dc.typeArticleen_US
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