Optimization and evaluation of facial recognition models for Williams-Beuren syndrome

doi:10.1007/s00431-024-05646-9

	Optimization and evaluation of facial recognition models for Williams-Beuren syndrome
	Huang, Pingchuan 1; Huang, Jinze 2; Huang, Yulu 1; Yang, Maohong 3; Kong, Ran 1; Sun, Haomiao 4,5; Han, Jin 6; Guo, Huiming 7,8; Wang, Shushui 1,3
	2024-06-14
发表期刊	EUROPEAN JOURNAL OF PEDIATRICS
ISSN	0340-6199
页码	12
摘要	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.Conclusion: 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. What Is Known:center dot 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.center dot Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS.What Is New:center dot 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.center dot 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.
关键词	Williams-Beuren syndrome Genetic syndrome VGG-19BN Artificial intelligence Automated facial recognition
DOI	10.1007/s00431-024-05646-9
收录类别	SCI
语种	英语
资助项目	GuangDong Basic and Applied Basic Research Foundation
WOS研究方向	Pediatrics
WOS类目	Pediatrics
WOS记录号	WOS:001245968100001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39923
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Han, Jin; Guo, Huiming; Wang, Shushui
作者单位	1.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Guangdong Cardiovasc Inst, Dept Pediat Cardiol, Guangzhou, Guangdong, Peoples R China 2.NYU, Courant Inst Math Sci, New York, NY USA 3.Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Pediat Cardiol, Guangzhou, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Beijing, Peoples R China 6.Guangzhou Women & Childrens Med Ctr, Prenatal Diag Ctr, Guangzhou, Guangdong, Peoples R China 7.Guangdong Acad Med Sci, Guangdong Cardiovasc Inst, Guangdong Prov Peoples Hosp, Dept Cardiac Surg, Guangzhou, Guangdong, Peoples R China 8.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Guangdong Cardiovasc Inst, Guangdong Prov Key Lab South China Struct Heart Di, Guangzhou, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Huang, Pingchuan,Huang, Jinze,Huang, Yulu,et al. Optimization and evaluation of facial recognition models for Williams-Beuren syndrome[J]. EUROPEAN JOURNAL OF PEDIATRICS,2024:12.
APA	Huang, Pingchuan.,Huang, Jinze.,Huang, Yulu.,Yang, Maohong.,Kong, Ran.,...&Wang, Shushui.(2024).Optimization and evaluation of facial recognition models for Williams-Beuren syndrome.EUROPEAN JOURNAL OF PEDIATRICS,12.
MLA	Huang, Pingchuan,et al."Optimization and evaluation of facial recognition models for Williams-Beuren syndrome".EUROPEAN JOURNAL OF PEDIATRICS (2024):12.