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Hsieh, T.C.* ; Bar-Haim, A.* ; Moosa, S.* ; Ehmke, N.* ; Gripp, K.W.* ; Pantel, J.T.* ; Danyel, M.* ; Mensah, M.A.* ; Horn, D.* ; Rosnev, S.* ; Fleischer, N.* ; Bonini, G.* ; Hustinx, A.* ; Schmid, A.* ; Knaus, A.* ; Javanmardi, B.* ; Klinkhammer, H.* ; Lesmann, H.* ; Sivalingam, S.* ; Kamphans, T.* ; Meiswinkel, W.* ; Ebstein, F.* ; Kruger, E.* ; Küry, S.* ; Bézieau, S.* ; Schmidt, A.* ; Peters, S.* ; Engels, H.* ; Mangold, E.* ; Kreiß, M.* ; Cremer, K.* ; Perne, C.* ; Betz, R.C.* ; Bender, T.* ; Grundmann-Hauser, K.* ; Haack, T.B.* ; Wagner, M. ; Brunet, T. ; Bentzen, H.B.* ; Averdunk, L.* ; Coetzer, K.C.* ; Lyon, G.J.* ; Spielmann, M.* ; Schaaf, C.P.* ; Mundlos, S.* ; Nöthen, M.M.* ; Krawitz, P.M.*

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Nat. Genet. 54, 349-357 (2022)
Postprint DOI PMC
Open Access Green
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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Intellectual Disability Syndrome; De-novo Mutations; Genetic-disorders; Variants; Delay
ISSN (print) / ISBN 1061-4036
e-ISSN 1546-1718
Journal Nature Genetics
Quellenangaben Volume: 54, Issue: 3, Pages: 349-357 Article Number: , Supplement: ,
Publisher Nature Publishing Group
Publishing Place New York, NY
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Neurogenomics (ING)
Grants Deutsche Forschungsgemeinschaft (German Research Foundation)