A large fraction of monogenic disorders causes craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate 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 created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 17,560 patients with 1,115 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism, as well as enable the delineation of novel phenotypes.

中文翻译：

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GestaltMatcher：克服使用面部表型描述符的罕见疾病匹配的限制

很大一部分单基因疾病会导致具有特征面部形态的颅面异常。在计算机辅助的下一代表型分析工具（例如 DeepGestalt）的支持下，可以更有效地诊断这些疾病。这些工具通过对数千张患者照片的训练，学会了将面部表型与潜在的综合征联系起来。然而，这种“监督”方法意味着只有当疾病是训练集的一部分时才有可能进行诊断。为了提高对极罕见疾病的识别能力，我们创建了 GestaltMatcher，它使用基于 DeepGestalt 框架的深度卷积神经网络。我们使用 17,560 名患有 1,115 种罕见疾病的患者的照片来定义“临床面部表型空间”。表型空间中病例之间的距离定义了综合征相似性，允许测试患者与分子诊断相匹配，即使该疾病未包含在训练集中。还可以检测具有先前未知疾病基因的患者之间的相似性。因此，结合突变数据，GestaltMatcher 可以加速对极罕见疾病和面部畸形患者的临床诊断，并能够描绘新的表型。