Syndrome identification based on 2D analysis software

• Published in: European journal of human genetics : EJHG Vol. 14; no. 10; pp. 1082 - 1089
• Main Authors: BOEHRINGER, Stefan, VOLLMAR, Tobias, TASSE, Christiane, WURTZ, Rolf P, GILLESSEN-KAESBACH, Gabriele, HORSTHEMKE, Bernhard, WIECZOREK, Dagmar
• Format: Journal Article
• Language: English
• Published: Avenel, NJ Nature Publishing 01.10.2006; Nature Publishing Group
• Subjects: Adolescent; Adult; Automation; Biological and medical sciences; Child; Child, Preschool; Children; Cholesterol; Classification; Congenital defects; Craniofacial Abnormalities - diagnosis; Data acquisition; Diagnosis; Diagnosis, Computer-Assisted - methods; Digital imaging; Distance learning; Facies; Female; Fragile X syndrome; General aspects. Genetic counseling; Humans; Infant; Male; Medical genetics; Medical sciences; Mental retardation; Morphology; Mucopolysaccharidosis; Opitz syndrome; Pattern Recognition, Automated; Prader-Willi syndrome; Reproducibility of Results; Software; Syndrome; Germany; Learning; facial appearance; Discrimination; Genetics; Identification; Software; statistical discrimination; Diagnosis; Face; Syndrome; syndrome diagnosis
• ISSN: 1018-4813; 1476-5438
• DOI: 10.1038/sj.ejhg.5201673

Clinical evaluation of children with developmental delay continues to present a challenge to the clinicians. In many cases, the face provides important information to diagnose a condition. However, database support with respect to facial traits is limited at present. Computer-based analyses of 2D and 3D representations of faces have been developed, but it is unclear how well a larger number of conditions can be handled by such systems. We have therefore analysed 2D pictures of patients each being affected with one of 10 syndromes (fragile X syndrome; Cornelia de Lange syndrome; Williams-Beuren syndrome; Prader-Willi syndrome; Mucopolysaccharidosis type III; Cri-du-chat syndrome; Smith-Lemli-Opitz syndrome; Sotos syndrome; Microdeletion 22q11.2; Noonan syndrome). We can show that a classification accuracy of >75% can be achieved for a computer-based diagnosis among the 10 syndromes, which is about the same accuracy achieved for five syndromes in a previous study. Pairwise discrimination of syndromes ranges from 80 to 99%. Furthermore, we can demonstrate that the criteria used by the computer decisions match clinical observations in many cases. These findings indicate that computer-based picture analysis might be a helpful addition to existing database systems, which are meant to assist in syndrome diagnosis, especially as data acquisition is straightforward and involves off-the-shelf digital camera equipment.