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

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.

Pitfalls and Promise of 3-dimensional Image Comparison for Craniofacial Surgical Assessment.

Three-dimensional (3D) photography is becoming widely used in plastic surgery. It provides an accurate and reproducible record of the facial surface anatomy and could be a versatile tool for treatment planning and assessment. However, the existing software tools available for the assessment of 3D facial imaging often give highly misleading results. The goal of this special topic article is to give clinicians an insight into methods of 3D image assessment and explain the reasons why results may be misleading. We point toward the advantages of an alternative approach using "nonrigid surface registration" for the comparison of pre- and postsurgical images. This approach is compared with the regular rigid surface registration, and this is illustrated by the assessment of a child with Crouzon syndrome before and after LeFort III osteotomy and distraction. Findings of the standard method imply that changes have occurred that are anatomically not possible, whereas the alternative approach indicates realistic changes. Furthermore, we demonstrate an exciting capacity of 3D image analysis to construct reference populations of normal head size and shape. These can be used to assess the parts of the head that are normal and abnormal pre- and posttreatment of the same child. We conclude that, while 3D image analysis has great potential in surgical assessment, existing software does not always give an adequate assessment. Collaboration among surgeons and engineering and computer science specialists should be encouraged. This way, more comprehensive and accurate techniques in patient assessment and surgical planning can be developed and applied in clinical practice.

Modelling 3D craniofacial growth trajectories for population comparison and classification illustrated using sex-differences.

Many disorders present with characteristic abnormalities of the craniofacial complex. Precise descriptions of how and when these abnormalities emerge and change during childhood and adolescence can inform our understanding of their underlying pathology and facilitate diagnosis from craniofacial shape. In this paper we develop a framework for analysing how anatomical differences between populations emerge and change over time, and for binary group classification that adapts to the age of each participant. As a proxy for a disease-control comparison we use a database of 3D photographs of normally developing boys and girls to examine emerging sex-differences. Essentially we define 3D craniofacial 'growth curves' for each sex. Differences in the forehead, upper lip, chin and nose emerge primarily from different growth rates between the groups, whereas differences in the buccal region involve different growth directions. Differences in the forehead, buccal region and chin are evident before puberty, challenging the view that sex differences result from pubertal hormone levels. Classification accuracy was best for older children. This paper represents a significant methodological advance for the study of facial differences between growing populations and comprehensively describes developing craniofacial sex differences.