1. GestaltMatcher facilitates rare disease matching using facial phenotype descriptors
- Author
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Tzung-Chien Hsieh, Aviram Bar-Haim, Shahida Moosa, Nadja Ehmke, Karen W. Gripp, Jean Tori Pantel, Magdalena Danyel, Martin Atta Mensah, Denise Horn, Stanislav Rosnev, Nicole Fleischer, Guilherme Bonini, Alexander Hustinx, Alexander Schmid, Alexej Knaus, Behnam Javanmardi, Hannah Klinkhammer, Hellen Lesmann, Sugirthan Sivalingam, Tom Kamphans, Wolfgang Meiswinkel, Frédéric Ebstein, Elke Krüger, Sébastien Küry, Stéphane Bézieau, Axel Schmidt, Sophia Peters, Hartmut Engels, Elisabeth Mangold, Martina Kreiß, Kirsten Cremer, Claudia Perne, Regina C. Betz, Tim Bender, Kathrin Grundmann-Hauser, Tobias B. Haack, Matias Wagner, Theresa Brunet, Heidi Beate Bentzen, Luisa Averdunk, Kimberly Christine Coetzer, Gholson J. Lyon, Malte Spielmann, Christian P. Schaaf, Stefan Mundlos, Markus M. Nöthen, and Peter M. Krawitz
- Subjects
Genetics - 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 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.
- Published
- 2022