GHGA Vorlesungsreihe: Peter Krawitz (virtuell)
Peter Krawitz vom Universität Bonn sprach im Rahmen der GHGA-Vorlesungsreihe ("Advances in Data-Driven Biomedicine") am 19. Juli 2023 über „GestaltMatcher and its data portal - Overcoming the limits of rare disease matching using facial phenotypic descriptors“.
Sehen Sie sich diesen Vortrag hier an.
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. The data portal GestaltMatcher DB can also be used to query for any medical images by gene or disorder.