PDF(Pubmed)
Abstract：
Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic \"elfin\" facial gestalt. The \"elfin\" facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs. Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs. Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed. Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models. Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.
摘要：
背景：威廉姆斯-贝伦综合征（WBS）是一种罕见的遗传综合征，具有特征性的“小精灵”面部格式塔。“小精灵”的面部特征包括宽阔的前额，眶周浮肿，扁平鼻梁,短的上翘鼻子，宽嘴巴,厚厚的嘴唇，还有尖尖的下巴.最近,深度卷积神经网络（CNN）已成功应用于面部识别以诊断遗传综合征。然而,关于使用深度CNN的WBS面部识别的研究很少。目的:构建基于深度CNN的WBS人脸自动识别模型。方法：该研究招募了104名WBS儿童，91例其他遗传综合征，145个健康的孩子照片数据集仅使用来自每个参与者的一张正面面部照片。通过采用VGG-16，VGG-19，ResNet-18，ResNet-34和MobileNet-V2架构，构建了五个用于WBS的人脸识别框架。分别。ImageNet迁移学习用于避免过度拟合。通过五次交叉验证评估了面部识别模型的分类性能，并与人类专家进行了比较。结果:构建了5个WBS人脸识别框架。VGG-19模型实现了最佳性能。准确性,精度,召回，F1得分,VGG-19模型的曲线下面积（AUC）为92.7±1.3％，94.0±5.6%,81.7±3.6%，87.2±2.0%,和89.6±1.3%，分别。最高的准确度,精度,召回，F1得分,人类专家的AUC分别为82.1、65.9、85.6、74.5和83.0％，分别。每个人类专家的AUC都低于VGG-16的AUC（88.6±3.5％），VGG-19(89.6±1.3%),ResNet-18(83.6±8.2%)，和ResNet-34(86.3±4.9%)型号。结论：这项研究强调了在临床实践中使用深层CNN诊断WBS的可能性。基于VGG-19的面部识别框架可以在WBS诊断中发挥重要作用。迁移学习技术可以帮助构建具有小规模数据集的遗传综合征的面部识别模型。
