Dense surface models can be used to analyze 3D facial morphology by establishing a correspondence of thousands of points across each 3D face image. The models provide dramatic visualizations of 3D face-shape variation with potential for training physicians to recognize the key components of particular syndromes. We demonstrate their use to visualize and recognize shape differences in a collection of 3D face images that includes 280 controls (2 weeks to 56 years of age), 90 individuals with Noonan syndrome (NS) (7 months to 56 years), and 60 individuals with velo-cardio-facial syndrome (VCFS; 3 to 17 years of age). Ten-fold cross-validation testing of discrimination between the three groups was carried out on unseen test examples using five pattern recognition algorithms (nearest mean, C5.0 decision trees, neural networks, logistic regression, and support vector machines). For discriminating between individuals with NS and controls, the best average sensitivity and specificity levels were 92 and 93% for children, 83 and 94% for adults, and 88 and 94% for the children and adults combined. For individuals with VCFS and controls, the best results were 83 and 92%. In a comparison of individuals with NS and individuals with VCFS, a correct identification rate of 95% was achieved for both syndromes. This article contains supplementary material, which may be viewed at the American Journal of Medical Genetics website at http://www.interscience.wiley.com/jpages/0148-7299/suppmat/index.html.
Many genetic syndromes involve a facial gestalt that suggests a preliminary diagnosis to an experienced clinical geneticist even before a clinical examination and genotyping are undertaken. Previously, using visualization and pattern recognition, we showed that dense surface models (DSMs) of full face shape characterize facial dysmorphology in Noonan and in 22q11 deletion syndromes. In this much larger study of 696 individuals, we extend the use of DSMs of the full face to establish accurate discrimination between controls and individuals with Williams, Smith-Magenis, 22q11 deletion, or Noonan syndromes and between individuals with different syndromes in these groups. However, the full power of the DSM approach is demonstrated by the comparable discriminating abilities of localized facial features, such as periorbital, perinasal, and perioral patches, and the correlation of DSM-based predictions and molecular findings. This study demonstrates the potential of face shape models to assist clinical training through visualization, to support clinical diagnosis of affected individuals through pattern recognition, and to enable the objective comparison of individuals sharing other phenotypic or genotypic properties.
This study evaluated the reproducibility of 24 soft tissue landmarks on six three-dimensional (3D) facial scans. The scans were taken on a DSP400 facial scanner and were viewed using a customized software program. Intraoperator data were obtained by one researcher placing the 24 landmarks on all six scans a total of 30 times. Thirty different orthodontists of varying experience were then asked to place all 24 landmarks on each of the six facial scans in order to establish interoperator reproducibility. The standard deviations (SDs) from the mean were calculated from the data for each individual landmark in the x-, y-, and z-axes. For the intraoperator data, 12 of the 24 landmarks were found to be reproducible to within a 1 mm SD for each plane of space. The interoperator data showed lower reproducibility with just two landmarks showing less than a 1 mm SD in all three planes of space. Familiarity with 3D facial scans and associated software programs is important in improving reproducibility. In addition, the landmarks investigated in this study included those not often used. It is suggested that landmarks showing poor reproducibility for both inter- and intraoperator data should be avoided, if at all possible, or at least used with caution.
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