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Title:

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Document type:
Article; Journal Article
Author(s):
Hsieh, Tzung-Chien; Bar-Haim, Aviram; Moosa, Shahida; Ehmke, Nadja; Gripp, Karen W; Pantel, Jean Tori; Danyel, Magdalena; Mensah, Martin Atta; Horn, Denise; Rosnev, Stanislav; Fleischer, Nicole; Bonini, Guilherme; Hustinx, Alexander; Schmid, Alexander; Knaus, Alexej; Javanmardi, Behnam; Klinkhammer, Hannah; Lesmann, Hellen; Sivalingam, Sugirthan; Kamphans, Tom; Meiswinkel, Wolfgang; Ebstein, Frédéric; Krüger, Elke; Küry, Sébastien; Bézieau, Stéphane; Schmidt, Axel; Peters, Sophia; Engels, Hartmu...     »
Abstract:
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...     »
Journal title abbreviation:
Nat Genet
Year:
2022
Journal volume:
54
Journal issue:
3
Pages contribution:
349-357
Fulltext / DOI:
doi:10.1038/s41588-021-01010-x
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/35145301
Print-ISSN:
1061-4036
TUM Institution:
1310; 183; Institut für Humangenetik
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