The assessment of children with autism based on computer vision

The assessment of children with autism based on computer vision | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The assessment of children with autism based on computer vision Ruisheng Ran, Wei Liang, Shan Deng, Xin Fan, Kai Shi, Ting Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4008436/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This paper presents a computer vision-based method for assessing and predicting autism spectrum disorders (ASDs).Traditional diagnostic methods for ASD have disadvantages such as high cost, strong subjectivity, and long time, but thismethod is convenient, low cost, and avoids some defects of traditional diagnostic methods. The research team designed asocial interaction scenario and collected an autistic child Face dataset (ACFD), then used computer vision methods to extractinformation about the children’s faces. On this basis, multiple features of children were obtained from four aspects: facialappearance time, eye concentration analysis, response time to name calls, and emotional expression ability, which were usedto assess and compare children with autism and typical development (TD). Finally, multiple features were linked together andmachine learning methods were used to classify children. The experimental results show that multiple features can reflect thetypical symptoms of children, and the classification accuracy of the prediction model is up to 92.16%, which proves that theautomatic recognition method can provide auxiliary diagnosis and data support for doctors. This method provides a new ideaand direction for early diagnosis and intervention of ASD, which is of great significance for improving the quality of life andtreatment effect of children with ASD. Health sciences/Medical research/Biomarkers/Predictive markers Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Aug, 2024 Reviews received at journal 07 Aug, 2024 Reviewers agreed at journal 04 Aug, 2024 Reviews received at journal 17 May, 2024 Reviewers agreed at journal 17 May, 2024 Reviewers agreed at journal 17 May, 2024 Reviewers invited by journal 07 Apr, 2024 Editor assigned by journal 02 Apr, 2024 Editor invited by journal 19 Mar, 2024 Submission checks completed at journal 19 Mar, 2024 First submitted to journal 03 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4008436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":281553645,"identity":"3c994daf-8597-4421-9ec1-760fb6a604c0","order_by":0,"name":"Ruisheng 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