The final, formatted version of the article will be published soon.
ORIGINAL RESEARCH article
Front. Neuroinform.
Volume 18 - 2024 |
doi: 10.3389/fninf.2024.1435091
This article is part of the Research Topic Improving Autism Spectrum Disorder Diagnosis Using Machine Learning Techniques View all 4 articles
Can Micro-expressions Be Used as a Biomarker for Autism Spectrum Disorder?
Provisionally accepted- 1 University at Albany, Albany, United States
- 2 West Virginia University, Morgantown, West Virginia, United States
- 3 Washington University in St. Louis, St. Louis, Missouri, United States
- 4 Congressionally Directed Medical Research Programs, Fort Detrick, MD 21702, United States
- 5 California Institute of Technology, Pasadena, California, United States
Early and accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective intervention, yet it remains a significant challenge due to its complexity and variability. This study introduces a novel machine-learning (ML) framework that advances ASD diagnostics by focusing on facial micro-expressions. Micro-expressions are rapid, involuntary facial movements indicative of underlying emotional states. We applied cutting-edge algorithms to detect and analyze these micro-expressions from video data, aiming to identify distinctive patterns that could differentiate individuals with ASD from typically developing peers. Our computational approach included three key components: 1) micro-expression spotting using Shallow Optical Flow Three-stream CNN (SOFTNet), 2) feature extraction via Micron-BERT, and 3) classification with majority voting of three competing models (MLP, SVM, and ResNet). Despite the sophisticated methodology, our experimental results suggest that the ML framework's ability to identify ASD-specific patterns reliably was limited by the quality of video data. This limitation raised concerns about the efficacy of using micro-expressions for ASD diagnostics and pointed to the necessity for enhanced video data quality. Consequently, our research has provided a cautious evaluation of micro-expression diagnostic value, underscoring the need for further advancements in behavioral imaging and multimodal AI technology to leverage the full capabilities of ML in an ASD-specific clinical context.
Keywords: Autism spectrum disorder (ASD), Face Videos, Micro-expressions, Interpretable machine learning, Autism Diagnostic Observation Schedule (ADOS)
Received: 19 May 2024; Accepted: 03 Sep 2024.
Copyright: © 2024 Li, Ruan, Zhang, Yu, Li, Hu, Webster, Paul and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xin Li, University at Albany, Albany, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.