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, Peter M Krawitz
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...
March 2022: Nature Genetics