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Clin Genet. 2016 May;89(5):557-63. doi: 10.1111/cge.12716. Epub 2016 Jan 25.

Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis.

Author information

1
Medical Genetics Department, Schneider Children's Medical Center of Israel, Rabin Medical Center, Petah Tikva, Israel.
2
Felsenstein Medical Research Center, Petah Tikva, Israel.
3
Tel Aviv University, Tel Aviv, Israel.
4
FDNA Inc., Boston, MA, USA.
5
Laboratory of Medical Cytogenetics and Molecular Genetics, Istituto Auxologico Italiano, Milan, Italy.
6
Department of Health Sciences, Medical Genetics, University of Milano, Milan, Italy.
7
Division of Human Molecular Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
8
The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Abstract

Facial analysis systems are becoming available to healthcare providers to aid in the recognition of dysmorphic phenotypes associated with a multitude of genetic syndromes. These technologies automatically detect facial points and extract various measurements from images to recognize dysmorphic features and evaluate similarities to known facial patterns (gestalts). To evaluate such systems' usefulness for supporting the clinical practice of healthcare professionals, the recognition accuracy of the Cornelia de Lange syndrome (CdLS) phenotype was examined with FDNA's automated facial dysmorphology novel analysis (FDNA) technology. In the first experiment, 2D facial images of CdLS patients with either an NIPBL or SMC1A gene mutation as well as non-CdLS patients which were assessed by dysmorphologists in a previous study were evaluated by the FDNA technology; the average detection rate of experts was 77% while the system's detection rate was 87%. In the second study, when a new set of NIPBL, SMC1A and non-CdLS patient photos was evaluated, the detection rate increased to 94%. The results from both studies indicated that the system's detection rate was comparable to that of dysmorphology experts. Therefore, utilizing such technologies may be a useful tool in a clinical setting.

KEYWORDS:

CdLS; FDNA; automated facial recognition; clinical genetics; dysmorphology

PMID:
26663098
DOI:
10.1111/cge.12716
[Indexed for MEDLINE]

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