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Syndromic genetic conditions, in aggregate, affect 8% of the population.sup.1. Many syndromes have recognizable facial features.sup.2 that are highly informative to clinical geneticists.sup.3-5. Recent studies show that facial analysis technologies measured up to the capabilities of expert clinicians in syndrome identification.sup.6-9. However, these technologies identified only a few disease phenotypes, limiting their role in clinical settings, where hundreds of diagnoses must be considered. Here we present a facial image analysis framework, DeepGestalt, using computer vision and deep-learning algorithms, that quantifies similarities to hundreds of syndromes. DeepGestalt outperformed clinicians in three initial experiments, two with the goal of distinguishing subjects with a target syndrome from other syndromes, and one of separating different genetic subtypes in Noonan syndrome. On the final experiment reflecting a real clinical setting problem, DeepGestalt achieved 91% top-10 accuracy in identifying the correct syndrome on 502 different images. The model was trained on a dataset of over 17,000 images representing more than 200 syndromes, curated through a community-driven phenotyping platform. DeepGestalt potentially adds considerable value to phenotypic evaluations in clinical genetics, genetic testing, research and precision medicine. A deep-learning algorithm, trained on over 17,000 real-world patient facial images, achieves high accuracy in identifying rare genetic disorders., Author(s): Yaron Gurovich [sup.1] , Yair Hanani [sup.1] , Omri Bar [sup.1] , Guy Nadav [sup.1] , Nicole Fleischer [sup.1] , Dekel Gelbman [sup.1] , Lina Basel-Salmon [sup.2] [sup.3] , [...]