GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
received:
31
12
2020
accepted:
16
12
2021
pubmed:
12
2
2022
medline:
28
4
2022
entrez:
11
2
2022
Statut:
ppublish
Résumé
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this 'supervised' approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes.
Identifiants
pubmed: 35145301
doi: 10.1038/s41588-021-01010-x
pii: 10.1038/s41588-021-01010-x
pmc: PMC9272356
mid: NIHMS1815116
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
349-357Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM133408
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
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