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Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome

Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one...

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Autores principales: Ciancia, Silvia, Goedegebuure, Wesley J., Grootjen, Lionne N., Hokken-Koelega, Anita C. S., Kerkhof, Gerthe F., van der Kaay, Daniëlle C. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257592/
https://www.ncbi.nlm.nih.gov/pubmed/36947243
http://dx.doi.org/10.1007/s00431-023-04937-x
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author Ciancia, Silvia
Goedegebuure, Wesley J.
Grootjen, Lionne N.
Hokken-Koelega, Anita C. S.
Kerkhof, Gerthe F.
van der Kaay, Daniëlle C. M.
author_facet Ciancia, Silvia
Goedegebuure, Wesley J.
Grootjen, Lionne N.
Hokken-Koelega, Anita C. S.
Kerkhof, Gerthe F.
van der Kaay, Daniëlle C. M.
author_sort Ciancia, Silvia
collection PubMed
description Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver–Russell syndrome (SRS) and Prader–Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1–15 years). Conclusion: In addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS.
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spelling pubmed-102575922023-06-12 Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome Ciancia, Silvia Goedegebuure, Wesley J. Grootjen, Lionne N. Hokken-Koelega, Anita C. S. Kerkhof, Gerthe F. van der Kaay, Daniëlle C. M. Eur J Pediatr Research Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver–Russell syndrome (SRS) and Prader–Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1–15 years). Conclusion: In addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS. Springer Berlin Heidelberg 2023-03-22 2023 /pmc/articles/PMC10257592/ /pubmed/36947243 http://dx.doi.org/10.1007/s00431-023-04937-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Ciancia, Silvia
Goedegebuure, Wesley J.
Grootjen, Lionne N.
Hokken-Koelega, Anita C. S.
Kerkhof, Gerthe F.
van der Kaay, Daniëlle C. M.
Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome
title Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome
title_full Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome
title_fullStr Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome
title_full_unstemmed Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome
title_short Computer-aided facial analysis as a tool to identify patients with Silver–Russell syndrome and Prader–Willi syndrome
title_sort computer-aided facial analysis as a tool to identify patients with silver–russell syndrome and prader–willi syndrome
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257592/
https://www.ncbi.nlm.nih.gov/pubmed/36947243
http://dx.doi.org/10.1007/s00431-023-04937-x
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