title: Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics.
abstract: BACKGROUND
  In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available.
  
  
  METHODS
  The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features.
  
  
  RESULTS
  The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p<0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively.
  
  
  CONCLUSIONS
  Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.
authors.affiliation: Department of Communication, Electronics, and Information Systems, Gülhane Military Medical Academy, Etlik, Ankara 06010, Turkey. Electronic address: kkuru@gata.edu.tr.
authors.forename: Kaya
authors.initials: K
authors.lastname: Kuru
authors.affiliation: School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BF, UK.
authors.forename: Mahesan
authors.initials: M
authors.lastname: Niranjan
authors.affiliation: Department of Medical Genetics, Gülhane Military Medical Academy, Etlik, Ankara 06010, Turkey.
authors.forename: Yusuf
authors.initials: Y
authors.lastname: Tunca
authors.affiliation: Institute of Informatics, Middle East Technical University, Balgat, Ankara 06531, Turkey.
authors.forename: Erhan
authors.initials: E
authors.lastname: Osvank
authors.affiliation: School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BF, UK.
authors.forename: Tayyaba
authors.initials: T
authors.lastname: Azim
mesh_terms: D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D005826:Genetics, Medical
keywords: Decision support system; Dysmorphology; Facial genotype–phenotype; Machine learning; Medical genetics; Principal components analysis; Visual data analysis