Computer-based recognition of dysmorphic faces.

Abstract

Genetic syndromes often involve craniofacial malformations. We have investigated whether a computer can recognize disease-specific facial patterns in unrelated individuals. For this, 55 photographs (256 x 256 pixel) of patients with mucopolysaccharidosis type III (n=6), Cornelia de Lange (n=12), fragile X (n=12), Prader-Willi (n=12), and Williams-Beuren (n=13) syndromes were preprocessed by a Gabor wavelet transformation. By comparing the feature vectors at 32 facial nodes, 42/55 (76%) of the patients were correctly classified. In another four patients (7%), the correct and an incorrect diagnosis scored equally well. Clinical geneticists who were shown the same photographs achieved a recognition rate of 62%. Our results prove that certain syndromes are associated with a specific facial pattern and that this pattern can be described in mathematical terms.