Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
FDNA Inc., Boston, MA, USA.
Nat Genet. 2022 Mar;54(3):349-357. doi: 10.1038/s41588-021-01010-x. Epub 2022 Feb 10.
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.
许多单基因疾病会导致特征性的面部形态。人工智能可以通过在数千张患者照片上进行训练，将面部表型与潜在综合征相关联，从而帮助医生识别这些模式。然而，这种“监督”方法意味着只有在疾病是训练集中的一部分时，才能进行诊断。为了提高对超罕见疾病的识别能力，我们开发了 GestaltMatcher，这是一种基于深度卷积神经网络的肖像编码器。使用 17560 名患有 1115 种罕见疾病的患者的照片来定义临床面部表型空间，其中病例之间的距离定义了综合征的相似性。在这里，我们表明，即使疾病未包含在训练集中，也可以将患者与具有相同分子诊断的其他患者相匹配。结合突变数据，GestaltMatcher 不仅可以加速超罕见疾病和面部畸形患者的临床诊断，还可以描绘出新的表型。
