Work

Autism screening for children based on appearance features across multiple paradigms

Year: 2025

Type: article

Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavior . Current ASD screening methods often focus on single paradigms or isolated features, limiting their screening accuracy. In this work, we propose a novel method for ASD screening by integrating appearance features such as gaze points, facial expressions, and head pose across multiple paradigms. The multiple paradigms including blank-overlap, person-gaze and exogenous-cueing are designed to elicit atypical behavioral patterns in ASD children. The extracted features from video data are then classified using a Long Short-Term Memory (LSTM) model. Our method achieves a classification accuracy of 0.932 and a sensitivity of 0.947 in differentiating ASD from typically developing (TD) children. The code and dataset related to this paper are available at https://github.com/theolsy/ASDTD .

Source: Displays

DOI: 10.1016/j.displa.2025.103049

Authors: Xiaofeng Lu, Siyao Yue, Chaozhen Li, Xia Yang, Yulin Wang, Zhi Liu

Institutions: Shanghai University, Changhai Hospital

Language: English

Topic: Autism Spectrum Disorder Research

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,450