Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology

In the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support particularly when only very few previous examples exist or in a remote environment where expert knowledge is not readily accessible. In this work we develop and demonstrate that accurate classification of dysmorphic faces is feasible by image processing of two dimensional face images. We test the proposed system on real patient image data by constructing a dataset of dysmorphic faces published in scholarly journals, hence having accurate diagnostic information about the syndrome. Our statistical methodology represents facial image data in terms of principal component analysis (PCA) and a leave one out evaluation scheme to quantify accuracy. The methodology has been tested with 15 syndromes including 75 cases, 5 examples per syndrome. A diagnosis success rate of 79% has been established. It can be concluded that a great number of syndromes indicating a characteristic pattern of facial anomalies can be typically diagnosed by employing computer-assisted machine learning algorithms since a face develops under the influence of many genes, particularly the genes causing syndromes.