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. Author manuscript; available in PMC: 2022 Jul 11.
Published in final edited form as: Nat Genet. 2022 Feb 10;54(3):349–357. doi: 10.1038/s41588-021-01010-x

Figure 2: Concept of GestaltMatcher.

Figure 2:

a, Architecture of a deep convolutional neural network consisting of an encoder and a classifier. Facial dysmorphic features of 299 frequent syndromes were used for supervised learning. The last fully connected layer in the feature encoder was taken as a Facial Phenotypic Descriptor (FPD), which forms a point in the Clinical Face Phenotype Space (CFPS). b, In the CFPS, the distance between each patient’s FPD can be considered as a measure of similarity of their facial phenotypic features. The distances can be further used for classifying ultra-rare disorders or matching patients with novel phenotypes. Take the input image shown in the figure as an example: the patient’s ultra-rare disease, which is caused by mutations in LEMD2, was not in the classifier, but was matched with another patient with the same ultra-rare disorder in the CFPS4.