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Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome

文献类型：期刊论文

作者Huang, Yulu1; Sun, Haomiao3,4; Chen, Qinchang2; Shen, Junjun2; Han, Jin5; Shan, Shiguang3,4; Wang, Shushui1,2
刊名BMC PEDIATRICS
出版日期2024-05-24
卷号24期号:1页码:9
关键词Noonan syndrome Genetic syndrome Convolution neural network Facial recognition Batch normalization
DOI10.1186/s12887-024-04827-7
英文摘要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.Objectives 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.Conclusion 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.
资助项目National Natural Science Foundation of China
WOS研究方向Pediatrics
语种英语
WOS记录号WOS:001230225500001
出版者BMC
源URL[http://119.78.100.204/handle/2XEOYT63/40066]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shan, Shiguang; Wang, Shushui
作者单位1.Guangdong Acad Med Sci, Guangdong Prov Peoples Hosp, Guangdong Cardiovasc Inst, Dept Pediat Cardiol, 96 Dongchuan Rd, Guangzhou, Guangdong, Peoples R China
2.Southern Med Univ, Guangdong Prov Peoples Hosp, Guangdong Acad Med Sci, Dept Pediat Cardiol, 106,Zhongshan 2nd Rd, Guangzhou, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, 6 South Sci Acad Rd, Beijing, Peoples R China
4.Univ Chinese Acad Sci, 80 Zhongguancun East Rd, Beijing, Peoples R China
5.Guangzhou Med Univ, Prenatal Diag Ctr, Guangzhou Women & Childrens Med Ctr, 9 Jinsui Rd, Guangzhou, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Yulu,Sun, Haomiao,Chen, Qinchang,et al. Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome[J]. BMC PEDIATRICS,2024,24(1):9.
APA Huang, Yulu.,Sun, Haomiao.,Chen, Qinchang.,Shen, Junjun.,Han, Jin.,...&Wang, Shushui.(2024).Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.BMC PEDIATRICS,24(1),9.
MLA Huang, Yulu,et al."Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome".BMC PEDIATRICS 24.1(2024):9.

入库方式： OAI收割

来源：计算技术研究所

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