Automatic Facial Recognition of Williams-Beuren Syndrome Based on Deep Convolutional Neural Networks.

Hui Liu,Ying-Yi Zheng,Hang Yang,Zi-Hua Mo,Min-Yin Lin,Long Wen,Zheng-Fu Zhang,Shu-Shui Wang,Dian Hong,Xiao-Wei Xu,Zhi-Wei Zhang,Jian Zhuang

doi:10.3389/fped.2021.648255

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.

Highlights

Williams-Beuren Syndrome (WBS) is a rare genetic syndrome, with an occurrence of ∼1 in 10,000 persons [1]
Five face recognition models for WBS were constructed with deep convolutional neural networks (CNNs) architectures combined with ImageNet transfer learning
Visual Geometry Group (VGG)-19 achieved the top value in terms of accuracy (92.7 ± 1.3%), precision (94.0 ± 5.6%), F1 score (87.2 ± 2.0%), and area under the ROC curve (AUC) (89.6 ± 1.3%)

Summary

Introduction

Williams-Beuren Syndrome (WBS) is a rare genetic syndrome, with an occurrence of ∼1 in 10,000 persons [1]. WBS is caused by the deletion of ∼1.5 million to 1.8 million base pairs on chromosome 7q11.23, which encompasses 26–28 genes [2] This multisystem disease can be confirmed by genetic testing, such as array comparative genomic hybridization, fluorescence in situ hybridization, quantitative real-time polymerase chain reaction, multiplex ligation-dependent probe amplification, and gene sequencing. Facial characteristics include broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, long philtrum, wide mouth, thick lips, and pointed chin [4, 5]. It will be beneficial to exploit a precise computeraided recognition tool for WBS diagnosis. 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. There is little research on WBS facial recognition using deep CNNs

Methods

Results

Conclusion