总的来说,估计有5%的人口患有遗传病。它们中的许多具有可以通过面部表型检测到的特征。Face2GeneCLINIC是用于遗传综合征患者面部表型的在线应用程序。DeepGestalt,驱动Face2Gene的神经网络,根据普通患者照片自动优先考虑综合征建议,有可能改善诊断过程。到目前为止,关于DeepGestalt质量的研究强调了其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检测阴性对照.
这项研究的目的是评估DeepGestalt的准确性与有和没有遗传综合症的个体的照片。此外,我们旨在提出一种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。
重新分析来自方便样本的具有遗传综合征(临床上或分子上确定的)诊断的个体的正面面部图像。每张照片都按年龄匹配,性别,和种族到一张没有遗传综合症的个体的照片。缺乏暗示遗传综合症的面部格式塔是由从事医学遗传学工作的医生确定的。照片是从在线报告中选择的,或者是我们为这项研究的目的而拍摄的。通过DeepGestalt版本19.1.7分析面部表型,通过Face2GeneCLINIC访问。此外,我们使用Python3.7设计了线性支持向量机(SVM),根据DeepGestalt的结果列表自动区分2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,并与那些没有遗传综合征的相同数量的面部图像相匹配。共分析646张图片。我们确认DeepGestalt的高灵敏度(前10位灵敏度:295/323,91%)。在没有颅面畸形综合征的个体中,DeepGestalt综合征的建议遵循非随机分布。在超过50%的非畸形图像的前30个建议中,总共出现了17个综合症。DeepGestalt的最高得分在综合征图像和对照图像之间存在差异(受试者工作特征[AUROC]曲线下面积0.72,95%CI0.68-0.76;P<.001)。在DeepGestalt结果向量上运行的线性SVM显示出更强的差异(AUROC0.89,95%CI0.87-0.92;P<.001)。
DeepGestalt将具有和不具有遗传综合症的个体的图像相当地分开。通过在DeepGestalt之上运行的SVM可以显着改善这种分离,从而支持遗传综合征患者的诊断过程。我们的发现有助于对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.