Introduction
Many genetic syndromes are associated with a distinctive facial gestalt which can be used to expedite the diagnostic process. Although high-throughput sequencing has helped to address the considerable heterogeneity of many syndromes in a single test, the rare expertise of dysmorphologists, which is still required for the data interpretation, is often the bottleneck. In recent years, advances in machine learning have enabled the development of NGP tools, that can be used to analyze facial dysmorphology in patient portrait photos (Ferry et al., 2014; Kuru et al., 2014; Gripp et al., 2016; Wang and Luo, 2016; Dudding-Byth et al., 2017; Hadj-Rabia et al., 2017; Valentine et al., 2017; Liehr et al., 2018; Gurovich et al., 2019; van der Donk et al., 2019; Hsieh et al., 2022). Amongst them is GestaltMatcher, which is a deep convolutional neural network that was trained on thousands of molecularly confirmed cases and achieves high accuracies in the identification of hundreds of syndromes (Hsieh et al., 2022). In this paper, we describe how the results of this artificial intelligence helped to solve a case with a typical phenotype of Koolen-de Vries syndrome but an unusual disease-causing mutation.