Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.
Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.
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DOI：
10.2196/19263
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发表时间：
2020-10-22
影响因子：
7.4
通讯作者：
Mensah MA
中科院分区：
文献类型：
--
作者：
Pantel JT;Hajjir N;Danyel M;Elsner J;Abad-Perez AT;Hansen P;Mundlos S;Spielmann M;Horn D;Ott CE;Mensah MA
总的来说，估计有5%的人口患有遗传性疾病。他们中的许多人的特征可以通过面部表型检测。Face 2Gene是一个在线应用程序，用于遗传综合征患者的面部表型分析。DeepGestalt是驱动Face 2Gene的神经网络，它会根据普通患者照片自动确定综合症建议的优先顺序，从而可能改善诊断过程。最近，关于DeepGestalt质量的研究强调了它在综合征患者中的敏感性。然而，确定诊断方法的准确性还需要测试阴性对照。这项研究的目的是评估DeepGestalt的准确性与个人的照片有和没有遗传综合征。此外，我们的目标是提出一个基于机器学习的框架，用于自动区分DeepGestalt在此类图像上的输出。从一个方便的样本诊断为遗传综合征（临床或分子）的个人的正面图像进行了重新分析。每一张照片都按照年龄、性别和种族与一张没有遗传综合征的照片相匹配。没有面部完形暗示遗传综合征是由从事医学遗传学工作的医生确定的。照片选自在线报告或由我们出于本研究的目的拍摄。通过DeepGestalt版本19.1.7分析面部表型，通过Face 2Gene分析。此外，我们使用Python 3.7设计了线性支持向量机（SVM），以根据DeepGestalt的结果列表自动区分两类照片。我们纳入了323名被诊断患有17种不同遗传综合征的患者的照片，并将其与相同数量的无遗传综合征的面部图像进行匹配，共分析了646张照片。我们证实了DeepGestalt的高灵敏度（前10名灵敏度：295/323，91%）。没有颅面畸形综合征的个体的DeepGestalt综合征建议遵循非随机分布。共有17个症状出现在超过50%的非畸形图像的前30个建议中。综合征图像和对照图像之间的DeepGestalt最高评分不同（受试者操作特征[AUROC]曲线下面积0.72，95%CI 0.68-0.76; P<0.001）。在DeepGestalt的结果向量上运行的线性SVM显示出更强的差异（AUROC 0.89，95%CI 0.87-0.92; P<.001）。DeepGestalt将有和没有遗传综合征的个体的图像分开。这种分离可以通过在DeepGestalt之上运行的SVM来显着改善，从而支持遗传综合征患者的诊断过程。我们的研究结果有助于对DeepGestalt结果的批判性解释，并可能有助于增强它和类似的计算机辅助面部表型分析工具。
Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.