First Italian experience using the automated craniofacial gestalt analysis on a cohort of pediatric patients with multiple anomaly syndromes.
Craniofacial
DeepGestalt
Malformation
Syndromes
Journal
Italian journal of pediatrics
ISSN: 1824-7288
Titre abrégé: Ital J Pediatr
Pays: England
ID NLM: 101510759
Informations de publication
Date de publication:
13 Jun 2022
13 Jun 2022
Historique:
received:
20
11
2021
accepted:
24
05
2022
entrez:
13
6
2022
pubmed:
14
6
2022
medline:
16
6
2022
Statut:
epublish
Résumé
In this study, we used the novel DeepGestalt technology powered by Face2Gene (FDNA Inc., MA, USA) in suggesting a correct diagnosis based on the facial gestalt of well-known multiple anomaly syndromes. Only molecularly characterized pediatric patients were considered in the present research. A total of 19 two-dimensional (2D) images of patients affected by several molecularly confirmed craniofacial syndromes (14 monogenic disorders and 5 chromosome diseases) and evaluated at the main involved Institution were analyzed using the Face2Gene CLINIC application (vs.19.1.3). Patients were cataloged into two main analysis groups (A, B) according to the number of clinical evaluations. Specifically, group A contained the patients evaluated more than one time, while in group B were comprised the subjects with a single clinical assesment. The algorithm's reliability was measured based on its capacity to identify the correct diagnosis as top-1 match, within the top-10 match and top-30 matches, only based on the uploaded image and not any other clinical finding or HPO terms. Failure was represented by the top-0 match. The correct diagnosis was suggested respectively in 100% (8/8) and 81% (9/11) of cases of group A and B, globally failing in 16% (3/19). The tested tool resulted to be useful in identifying the facial gestalt of a heterogeneous group of syndromic disorders. This study illustrates the first Italian experience with the next generation phenotyping technology, following previous works and providing additional observations.
Sections du résumé
BACKGROUND
BACKGROUND
In this study, we used the novel DeepGestalt technology powered by Face2Gene (FDNA Inc., MA, USA) in suggesting a correct diagnosis based on the facial gestalt of well-known multiple anomaly syndromes. Only molecularly characterized pediatric patients were considered in the present research.
SUBJECTS AND METHODS
METHODS
A total of 19 two-dimensional (2D) images of patients affected by several molecularly confirmed craniofacial syndromes (14 monogenic disorders and 5 chromosome diseases) and evaluated at the main involved Institution were analyzed using the Face2Gene CLINIC application (vs.19.1.3). Patients were cataloged into two main analysis groups (A, B) according to the number of clinical evaluations. Specifically, group A contained the patients evaluated more than one time, while in group B were comprised the subjects with a single clinical assesment. The algorithm's reliability was measured based on its capacity to identify the correct diagnosis as top-1 match, within the top-10 match and top-30 matches, only based on the uploaded image and not any other clinical finding or HPO terms. Failure was represented by the top-0 match.
RESULTS
RESULTS
The correct diagnosis was suggested respectively in 100% (8/8) and 81% (9/11) of cases of group A and B, globally failing in 16% (3/19).
CONCLUSION
CONCLUSIONS
The tested tool resulted to be useful in identifying the facial gestalt of a heterogeneous group of syndromic disorders. This study illustrates the first Italian experience with the next generation phenotyping technology, following previous works and providing additional observations.
Identifiants
pubmed: 35698205
doi: 10.1186/s13052-022-01283-w
pii: 10.1186/s13052-022-01283-w
pmc: PMC9195312
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
91Informations de copyright
© 2022. The Author(s).
Références
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