FDNA Inc., Boston, MA, USA.
Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Nat Med. 2019 Jan;25(1):60-64. doi: 10.1038/s41591-018-0279-0. Epub 2019 Jan 7.
Syndromic genetic conditions, in aggregate, affect 8% of the population. Many syndromes have recognizable facial features that are highly informative to clinical geneticists. Recent studies show that facial analysis technologies measured up to the capabilities of expert clinicians in syndrome identification. 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.
综合征遗传疾病总体上影响了 8%的人群。许多综合征都有明显的面部特征，这对临床遗传学家来说具有重要意义。最近的研究表明，面部分析技术在综合征识别方面与专家临床医生的能力相当。然而，这些技术仅能识别少数疾病表型，限制了它们在临床环境中的应用，因为在临床环境中需要考虑数百种诊断。在这里，我们提出了一种基于计算机视觉和深度学习算法的面部图像分析框架 DeepGestalt，它可以量化与数百种综合征的相似度。DeepGestalt 在三个初始实验中表现优于临床医生，其中两个实验的目的是将具有目标综合征的受试者与其他综合征区分开来，一个实验的目的是将 Noonan 综合征的不同遗传亚型区分开来。在反映真实临床环境问题的最后一个实验中，DeepGestalt 在对 502 张不同图像进行的正确综合征识别中达到了 91%的准确率。该模型是在一个包含超过 17000 张图像的数据集上进行训练的，这些图像代表了 200 多种综合征，是通过一个由社区驱动的表型平台进行整理的。DeepGestalt 在临床遗传学、基因检测、研究和精准医学中的表型评估中具有重要的应用价值。
