Abstract：
Williams-Beuren syndrome (WBS) is a rare genetic disorder characterized by special facial gestalt, delayed development, and supravalvular aortic stenosis or/and stenosis of the branches of the pulmonary artery. We aim to develop and optimize accurate models of facial recognition to assist in the diagnosis of WBS, and to evaluate their effectiveness by using both five-fold cross-validation and an external test set. We used a total of 954 images from 135 patients with WBS, 124 patients suffering from other genetic disorders, and 183 healthy children. The training set comprised 852 images of 104 WBS cases, 91 cases of other genetic disorders, and 145 healthy children from September 2017 to December 2021 at the Guangdong Provincial People\'s Hospital. We constructed six binary classification models of facial recognition for WBS by using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. Transfer learning was used to pre-train the models, and each model was modified with a variable cosine learning rate. Each model was first evaluated by using five-fold cross-validation and then assessed on the external test set. The latter contained 102 images of 31 children suffering from WBS, 33 children with other genetic disorders, and 38 healthy children. To compare the capabilities of these models of recognition with those of human experts in terms of identifying cases of WBS, we recruited two pediatricians, a pediatric cardiologist, and a pediatric geneticist to identify the WBS patients based solely on their facial images. We constructed six models of facial recognition for diagnosing WBS using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. The model based on VGG-19BN achieved the best performance in terms of five-fold cross-validation, with an accuracy of 93.74% ± 3.18%, precision of 94.93% ± 4.53%, specificity of 96.10% ± 4.30%, and F1 score of 91.65% ± 4.28%, while the VGG-16BN model achieved the highest recall value of 91.63% ± 5.96%. The VGG-19BN model also achieved the best performance on the external test set, with an accuracy of 95.10%, precision of 100%, recall of 83.87%, specificity of 93.42%, and F1 score of 91.23%. The best performance by human experts on the external test set yielded values of accuracy, precision, recall, specificity, and F1 scores of 77.45%, 60.53%, 77.42%, 83.10%, and 66.67%, respectively. The F1 score of each human expert was lower than those of the EfficientNet-b3 (84.21%), ResNet-50 (74.51%), VGG-16 (85.71%), VGG-16BN (85.71%), VGG-19 (83.02%), and VGG-19BN (91.23%) models.
CONCLUSIONS: The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate.
BACKGROUND: • The facial gestalt of WBS, often described as \"elfin,\" includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS.
BACKGROUND: • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.
CONCLUSIONS: The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate.
BACKGROUND: • The facial gestalt of WBS, often described as \"elfin,\" includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS.
BACKGROUND: • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.
摘要：
威廉姆斯-贝伦综合征（WBS）是一种罕见的遗传性疾病，以特殊的面部完形为特征，延迟发展，和主动脉瓣上狭窄或/和肺动脉分支狭窄。我们的目标是开发和优化准确的面部识别模型，以帮助诊断WBS，并通过使用五折交叉验证和外部测试集来评估其有效性。我们使用了135例WBS患者的954张图像，124名患有其他遗传疾病的患者，183个健康的孩子训练集包括104例WBS病例的852张图像，91例其他遗传性疾病，2017年9月至2021年12月在广东省人民医院就诊的145名健康儿童。我们通过使用EfficientNet-b3，ResNet-50，VGG-16，VGG-16BN构建了六个WBS面部识别的二元分类模型，VGG-19和VGG-19BN。迁移学习用于预先训练模型，每个模型都用可变余弦学习率进行了修改。首先通过使用五折交叉验证来评估每个模型,然后在外部测试集上进行评估。后者包含102张患有WBS的31名儿童的图像，33名患有其他遗传性疾病的儿童，38个健康的孩子为了将这些识别模型的能力与人类专家在识别WBS案例方面的能力进行比较，我们招募了两名儿科医生，一位儿科心脏病专家,和儿科遗传学家仅根据他们的面部图像来识别WBS患者。我们使用EfficientNet-b3，ResNet-50，VGG-16，VGG-16BN构建了六个面部识别模型来诊断WBS，VGG-19和VGG-19BN。基于VGG-19BN的模型在五重交叉验证方面取得了最佳性能，准确率为93.74%±3.18%，精度为94.93%±4.53%，特异性96.10%±4.30%，F1评分为91.65%±4.28%，而VGG-16BN模型达到了91.63%±5.96%的最高召回值。VGG-19BN型号在外部测试集上也取得了最佳性能，准确率为95.10%，精度100%,召回83.87%，特异性为93.42%，F1得分为91.23%。人类专家在外部测试集上的最佳性能产生了准确性值，精度,召回，特异性，F1得分为77.45%，60.53%，77.42%,83.10%,和66.67%，分别。每个人类专家的F1得分均低于EfficientNet-b3（84.21％），ResNet-50（74.51%），VGG-16（85.71%），VGG-16BN（85.71%），VGG-19（83.02%），和VGG-19BN（91.23%）型号。
结论：结果表明，面部识别技术可用于准确诊断WBS患者。基于VGG-19BN的面部识别模型在其临床诊断中起着至关重要的作用。它们的性能可以通过扩展训练数据集的大小来提高，优化所应用的CNN架构，并用可变余弦学习率修改它们。
背景：•WBS的面部完形，通常被描述为“小精灵，“包括宽阔的前额，眶周浮肿，扁平的鼻梁,丰满的脸颊，还有一个小下巴.•最近的研究已经证明了深度卷积神经网络作为WBS诊断工具的面部识别的潜力。
背景：•本研究开发了六种面部识别模型，EfficientNet-b3,ResNet-50,VGG-16,VGG-16BN,VGG-19和VGG-19BN,改善WBS诊断。•VGG-19BN模型实现了最佳性能，准确率为95.10%，特异性为93.42%。基于VGG-19BN的人脸识别模型在WBS的临床诊断中起着至关重要的作用。
结论：结果表明，面部识别技术可用于准确诊断WBS患者。基于VGG-19BN的面部识别模型在其临床诊断中起着至关重要的作用。它们的性能可以通过扩展训练数据集的大小来提高，优化所应用的CNN架构，并用可变余弦学习率修改它们。
背景：•WBS的面部完形，通常被描述为“小精灵，“包括宽阔的前额，眶周浮肿，扁平的鼻梁,丰满的脸颊，还有一个小下巴.•最近的研究已经证明了深度卷积神经网络作为WBS诊断工具的面部识别的潜力。
背景：•本研究开发了六种面部识别模型，EfficientNet-b3,ResNet-50,VGG-16,VGG-16BN,VGG-19和VGG-19BN,改善WBS诊断。•VGG-19BN模型实现了最佳性能，准确率为95.10%，特异性为93.42%。基于VGG-19BN的人脸识别模型在WBS的临床诊断中起着至关重要的作用。
