Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application.

Angelina Lu,Marek Perkowski

doi:10.3390/brainsci11111446

Abstract

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges. Early intervention for children with ASD can help to improve their intellectual ability and reduces autistic symptoms. Multiple clinical researches have suggested that facial phenotypic differences exist between ASD children and typically developing (TD) children. In this research, we propose a practical ASD screening solution using facial images through applying VGG16 transfer learning-based deep learning to a unique ASD dataset of clinically diagnosed children that we collected. Our model produced a 95% classification accuracy and 0.95 F1-score. The only other reported study using facial images to detect ASD was based on the Kaggle ASD Facial Image Dataset, which is an internet search-produced, low-quality, and low-fidelity dataset. Our results support the clinical findings of facial feature differences between children with ASD and TD children. The high F1-score achieved indicates that it is viable to use deep learning models to screen children with ASD. We concluded that the racial and ethnic-related factors in deep-learning based ASD screening with facial images are critical to solution viability and accuracy.

Highlights

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges according to the Centers for Disease Control and Prevention (CDC)
The VGG-16 embedding followed by the neural network model with two hidden layers achieved a classification accuracy of 93.3% and F1 score of 0.928, and it proved to be feasible to use this VGG-based deep-learning solution to detect ASD using facial images
The high classification accuracy of 95% and F1-score of 0.95 obtained by our deep learning model trained with the East Asian dataset indicates that it is viable to use children’s facial images as a low-cost solution to screen for ASD to achieve early intervention objectives

Summary

Introduction

Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication, and behavioral challenges according to the Centers for Disease Control and Prevention (CDC). The combined estimated ASD prevalence was 16.8 per 1000 (1 in 59) children in 2018, it was significantly higher among non-Latino White children (17.2 per 1000) than among non-Latino African American children (16.0 per 1000), Latino children (14.0 per 1000), and Asian/Pacific Islander children (13.5 per 1000) [1]. These delayed or misdiagnoses for minority races result in a loss of opportunity in early intervention for children with ASD. A recent cost-comparison study of early intensive behavioral intervention in the Netherlands suggested that lifetime cost savings could be over EUR 1 million per

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