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Automated syndrome detection in a set of clinical facial photographs
Author(s) -
Boehringer Stefan,
Guenther Manuel,
Sinigerova Stella,
Wurtz Rolf P.,
Horsthemke Bernhard,
Wieczorek Dagmar
Publication year - 2011
Publication title -
american journal of medical genetics part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.064
H-Index - 112
eISSN - 1552-4833
pISSN - 1552-4825
DOI - 10.1002/ajmg.a.34157
Subject(s) - medical diagnosis , set (abstract data type) , computer science , data set , artificial intelligence , clinical practice , exploit , software , clinical diagnosis , sample (material) , pattern recognition (psychology) , machine learning , medicine , radiology , clinical psychology , physical therapy , chemistry , computer security , chromatography , programming language
Computer systems play an important role in clinical genetics and are a routine part of finding clinical diagnoses but make it difficult to fully exploit information derived from facial appearance. So far, automated syndrome diagnosis based on digital, facial photographs has been demonstrated under study conditions but has not been applied in clinical practice. We have therefore investigated how well statistical classifiers trained on study data comprising 202 individuals affected by one of 14 syndromes could classify a set of 91 patients for whom pictures were taken under regular, less controlled conditions in clinical practice. We found a classification accuracy of 21% percent in the clinical sample representing a ratio of 3.0 over a random choice. This contrasts with a 60% accuracy or 8.5 ratio in the training data. Producing average images in both groups from sets of pictures for each syndrome demonstrates that the groups exhibit large phenotypic differences explaining discrepancies in accuracy. A broadening of the data set is suggested in order to improve accuracy in clinical practice. In order to further this goal, a software package is made available that allows application of the procedures and contributions toward an improved data set. © 2011 Wiley‐Liss, Inc.