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Titel:

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Dokumenttyp:
Article; Journal Article
Autor(en):
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...     »
Zeitschriftentitel:
Nat Genet
Jahr:
2022
Band / Volume:
54
Heft / Issue:
3
Seitenangaben Beitrag:
349-357
Volltext / DOI:
doi:10.1038/s41588-021-01010-x
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/35145301
Print-ISSN:
1061-4036
TUM Einrichtung:
1310; 183; Institut für Humangenetik
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