PDF(Pubmed)
Abstract：
BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects.
OBJECTIVE: This study develops advanced models to enhance the accuracy of diagnosis of NS.
METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians.
RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics.
CONCLUSIONS: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.
OBJECTIVE: This study develops advanced models to enhance the accuracy of diagnosis of NS.
METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians.
RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics.
CONCLUSIONS: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.
摘要：
背景：努南综合征（NS）是一种罕见的遗传性疾病，患有这种疾病的患者表现出面部形态，其特征是前额高，超端粒,上睑下垂,内上皮褶皱，向下倾斜的睑裂,高度拱形的腭，一个圆形的鼻尖,耳朵向后旋转。面部分析技术最近已被用于识别许多遗传综合征（GS）。然而,很少有研究根据受试者的面部特征来研究NS的识别。
目的：本研究开发了先进的模型来提高NS诊断的准确性。
方法：本研究共纳入1,892人，包括233名NS患者，863名患有其他GSs的患者，796名健康儿童。我们为每个受试者拍摄了1到10张正面照片来建立一个数据集，然后应用多任务卷积神经网络（MTCNN）进行数据预处理，以生成具有五个关键面部标志的标准化输出。ImageNet数据集用于预训练网络，以便它可以捕获可概括的特征并最大程度地减少数据浪费。随后，我们基于VGG16、VGG19、VGG16-BN构建了七个面部识别模型，VGG19-BN,ResNet50、MobileNet-V2和挤压和激励网络(SENet)架构。评估了七个模型的识别性能，并与六个医生的识别性能进行了比较。
结果：所有模型都表现出很高的准确性，精度,和特异性识别NS患者。VGG19-BN型号提供了最佳的整体性能，准确率为93.76%，精度为91.40%，特异性98.73%,F1得分为78.34%。VGG16-BN模型实现了0.9787的最高AUC值，而基于VGG架构的所有模型总体上都优于其他模型。六位医生的准确度得分最高，精度,特异性，F1评分为74.00%，75.00%,88.33%,和61.76%，分别。在所有指标上，每个面部识别模型的性能都优于最好的医生。
结论：计算机辅助面部识别模型可以提高NS的诊断率。基于VGG19-BN和VGG16-BN的模型可以在临床实践中诊断NS中起重要作用。
目的：本研究开发了先进的模型来提高NS诊断的准确性。
方法：本研究共纳入1,892人，包括233名NS患者，863名患有其他GSs的患者，796名健康儿童。我们为每个受试者拍摄了1到10张正面照片来建立一个数据集，然后应用多任务卷积神经网络（MTCNN）进行数据预处理，以生成具有五个关键面部标志的标准化输出。ImageNet数据集用于预训练网络，以便它可以捕获可概括的特征并最大程度地减少数据浪费。随后，我们基于VGG16、VGG19、VGG16-BN构建了七个面部识别模型，VGG19-BN,ResNet50、MobileNet-V2和挤压和激励网络(SENet)架构。评估了七个模型的识别性能，并与六个医生的识别性能进行了比较。
结果：所有模型都表现出很高的准确性，精度,和特异性识别NS患者。VGG19-BN型号提供了最佳的整体性能，准确率为93.76%，精度为91.40%，特异性98.73%,F1得分为78.34%。VGG16-BN模型实现了0.9787的最高AUC值，而基于VGG架构的所有模型总体上都优于其他模型。六位医生的准确度得分最高，精度,特异性，F1评分为74.00%，75.00%,88.33%,和61.76%，分别。在所有指标上，每个面部识别模型的性能都优于最好的医生。
结论：计算机辅助面部识别模型可以提高NS的诊断率。基于VGG19-BN和VGG16-BN的模型可以在临床实践中诊断NS中起重要作用。
