dc.contributor.author
Pantel, Jean Tori
dc.contributor.author
Hajjir, Nurulhuda
dc.contributor.author
Danyel, Magdalena
dc.contributor.author
Elsner, Jonas
dc.contributor.author
Abad-Perez, Angela Teresa
dc.contributor.author
Hansen, Peter
dc.contributor.author
Mundlos, Stefan
dc.contributor.author
Spielmann, Malte
dc.contributor.author
Horn, Denise
dc.contributor.author
Ott, Claus-Eric
dc.contributor.author
Mensah, Martin Atta
dc.date.accessioned
2021-01-20T09:13:19Z
dc.date.available
2021-01-20T09:13:19Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/28727
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-28475
dc.description.abstract
Background: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls.
Objective: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images.
Methods: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists.
Results: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001).
Conclusions: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.
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dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
facial phenotyping
en
dc.subject
facial recognition
en
dc.subject
medical genetics
en
dc.subject
diagnostic accuracy
en
dc.subject
genetic syndrome
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dc.subject
machine learning
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
e19263
dcterms.bibliographicCitation.doi
10.2196/19263
dcterms.bibliographicCitation.journaltitle
Journal of Medical Internet Research
dcterms.bibliographicCitation.number
10
dcterms.bibliographicCitation.originalpublishername
JMIR Publications
dcterms.bibliographicCitation.volume
22
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
33090109
dcterms.isPartOf.eissn
1438-8871