{"id":6439,"date":"2025-04-09T23:22:10","date_gmt":"2025-04-09T23:22:10","guid":{"rendered":"https:\/\/www.europesays.com\/uk\/6439\/"},"modified":"2025-04-09T23:22:10","modified_gmt":"2025-04-09T23:22:10","slug":"ai-and-vr-system-detects-autism-in-kids-with-over-85-accuracy","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/uk\/6439\/","title":{"rendered":"AI and VR System Detects Autism in Kids with Over 85% Accuracy"},"content":{"rendered":"<p><strong>Summary: <\/strong>Researchers have developed a virtual reality and AI-based system that can detect autism spectrum disorder (ASD) in young children with over 85% accuracy\u2014outperforming traditional assessment methods. The system observes children\u2019s motor movements and gaze patterns while they engage in tasks within immersive virtual environments, enabling more naturalistic responses than typical lab settings.<\/p>\n<p>Using a deep learning model, the system identifies behavioral biomarkers linked to ASD and delivers a diagnosis efficiently and affordably. This innovation could significantly expand access to early autism detection and lays the groundwork for studying other motor symptoms in ASD.<\/p>\n<p><strong>Key Facts:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><strong>High Accuracy:<\/strong> The VR-AI system achieved over 85% accuracy in detecting ASD.<\/li>\n<li><strong>Natural Interaction:<\/strong> Children\u2019s behaviors are assessed in realistic virtual environments, enhancing diagnostic validity.<\/li>\n<li><strong>Accessible Tools:<\/strong> The system uses commercially available cameras and screens, making widespread use feasible.<\/li>\n<\/ul>\n<p><strong>Source: <\/strong>UPV<\/p>\n<p><strong>A team from the Human-Tech Institute-Universitat Polit\u00e8cnica de Val\u00e8ncia has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence.\u00a0<\/strong><\/p>\n<p>The system has achieved an accuracy of over 85%, thus surpassing traditional methods of detecting autism in early childhood, which are usually based on psychological tests and interviews carried out manually.<\/p>\n<p>The results of the work of the UPV team have been published in the Expert Systems with Applications journal.<\/p>\n<p>  <img fetchpriority=\"high\" decoding=\"async\" width=\"1200\" height=\"800\" src=\"https:\/\/www.europesays.com\/uk\/wp-content\/uploads\/2025\/04\/AI-VR-ASD-neuroscience.jpg\" alt=\"This shows a child in a VR generated room.\"  \/> The researcher concludes that the \u2018ease with which this data can be collected and its high effectiveness in detecting autism make the motor activity a promising biomarker\u2019. Credit: Neuroscience News<\/p>\n<p>In the study, the team from the Human-Tech Institute analysed the movements of children performing multiple tasks in virtual reality to determine which artificial intelligence technique is most appropriate for identifying ASD.<\/p>\n<p>\u2018The use of virtual reality allows us to use recognisable environments that generate realistic and authentic responses, imitating how children interact in their daily lives.<\/p>\n<p>\u2018This is a significant improvement over laboratory tests, in which responses are often artificial. With virtual reality, we can study more genuine reactions and better understand the symptoms of autism,\u2019 says Mariano Alca\u00f1iz, director of the Human-Tech Institute at the UPV.<\/p>\n<p>The virtual system consists of projecting, on the walls of a room or a large-format screen, a simulated environment in which the child\u2019s image is integrated while performing multiple tasks, captured by a camera that analyses their movements.<\/p>\n<p>\u2018This method standardises the detection of autism by analysing biomarkers related to behaviour, motor activity and gaze direction.<\/p>\n<p>\u2018Our system only requires a large screen and a type of camera that is already on the market and is cheaper than the usual test-based evaluation method. Without doubt, it would facilitate access to diagnosis as it could be included in any early intervention space\u2019, emphasises Mariano Alca\u00f1iz.<\/p>\n<p><strong>New artificial intelligence model<\/strong><\/p>\n<p>On the other hand, as explained by the researcher Alberto Altozano, who developed the AI model together with Professor Javier Mar\u00edn, taking advantage of the experience acquired in the analysis of motor data, the UPV team compared traditional AI techniques with an innovative deep learning model.<\/p>\n<p>\u2018The results reveal that the proposed new model can identify ASD with greater precision and in a greater number of tasks within the VR experience,\u2019 says Altozano.<\/p>\n<p>Once the child\u2019s movements during the virtual experience have been automatically processed, the system establishes a diagnosis that, according to those responsible for the study, improves both the accuracy and the efficiency of conventional techniques.<\/p>\n<p><strong>Eight years of collaboration to improve early detection<\/strong><\/p>\n<p>Over the last eight years, the Human-Tech Institute of the UPV team has worked on perfecting the early detection of ASD, collaborating with the Red Cenit cognitive development centre, and developing and validating the semi-immersive system.<\/p>\n<p>Within this framework, the researcher Eleonora Minissi recently presented her doctoral thesis, in which not only was the virtual reality system validated through studies with autistic children, but also the effectiveness of the various biomarkers measured during the virtual experience was compared.<\/p>\n<p>Her research highlights that, despite the growing interest in social-visual attention in ASD, atypical motor patterns have received less diagnostic attention.<\/p>\n<p>The researcher concludes that the \u2018ease with which this data can be collected and its high effectiveness in detecting autism make the motor activity a promising biomarker\u2019.<\/p>\n<p>In addition, the latest results of the work of the Human-Tech Institute team suggest that the new AI can be adapted and trained to analyse the movements of ASD patients in other tasks.<\/p>\n<p>\u2018This opens the door to future explorations of the motor symptomatology of autism such as: what are the motor characteristics of autistic children when walking or talking?\u2019 adds Mariano Alca\u00f1iz.<\/p>\n<p>About this AI, ASD, and virtual reality research news<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Author: <\/strong><a href=\"http:\/\/neurosciencenews.com\/cdn-cgi\/l\/email-protection#412228242f222820013431376f2432\" target=\"_blank\" rel=\"noreferrer noopener\">Luis Zurano<\/a><br \/><strong>Source: <\/strong><a href=\"https:\/\/upv.es\" target=\"_blank\" rel=\"noreferrer noopener\">UPV<\/a><br \/><strong>Contact: <\/strong>Luis Zurano \u2013 UPV<br \/><strong>Image: <\/strong>The image is credited to Neuroscience News<\/p>\n<p class=\"has-background\" style=\"background-color:#ffffe8\"><strong>Original Research: <\/strong>Open access.<br \/>\u201c<a href=\"https:\/\/dx.doi.org\/10.1016\/j.eswa.2024.126295\" target=\"_blank\" rel=\"noreferrer noopener\">Introducing 3DCNN ResNets for ASD full-body kinematic assessment: A comparison with hand-crafted features<\/a>\u201d by Mariano Alca\u00f1iz et al. Expert Systems with Applications<\/p>\n<p><strong>Abstract<\/strong><\/p>\n<p><strong>Introducing 3DCNN ResNets for ASD full-body kinematic assessment: A comparison with hand-crafted features<\/strong><\/p>\n<p>Autism Spectrum Disorder (ASD) is characterized by challenges in social communication and restricted patterns, with motor abnormalities gaining traction for early detection.<\/p>\n<p>However, kinematic analysis in ASD is limited, often lacking robust validation and relying on hand-crafted features for single tasks, leading to inconsistencies across studies.<\/p>\n<p>End-to-end models have emerged as promising methods to overcome the need for feature engineering.<\/p>\n<p>Our aim is to propose a newly adapted 3DCNN ResNet from action recognition and compare it to widely used hand-crafted features for motor ASD assessment.<\/p>\n<p>Specifically, we developed a virtual reality environment with multiple motor tasks and trained models using both approaches.<\/p>\n<p>We prioritized a reliable validation framework with subject-wise nestedrepeated cross-validation.<\/p>\n<p>Results show the proposed model achieves a maximum accuracy of 85\u00b13%, outperforming state-of-the-art end-to-end models with short 1-to-3 min samples.<\/p>\n<p>Our comparative analysis with hand-crafted features shows feature-engineered models outperformed our end-to-end model in certain tasks.<\/p>\n<p>However, generalized linear mixed-effects models showed that our end-to-end model achieved a statistically higher mean AUC (0.80\u00b10.03) and Sensitivity (66\u00b13%), while showing less variability across all VR tasks, demonstrating domain generalization and reliability.<\/p>\n<p>These findings show that end-to-end models enable less variable and context-independent ASD classification without requiring domain knowledge or task specificity.<\/p>\n<p>However, they also recognize the effectiveness of hand-crafted features in specific task scenarios.<\/p>\n","protected":false},"excerpt":{"rendered":"Summary: Researchers have developed a virtual reality and AI-based system that can detect autism spectrum disorder (ASD) in&hellip;\n","protected":false},"author":2,"featured_media":6440,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3163],"tags":[323,1942,662,663,648,215,3725,649,3690,219,654,220,2996,53,16,15,3726,3243,3244],"class_list":{"0":"post-6439","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-asd","11":"tag-autism","12":"tag-brain-development","13":"tag-brain-research","14":"tag-deep-learning","15":"tag-developmental-neuroscience","16":"tag-machine-learning","17":"tag-neurobiology","18":"tag-neurodevelopment","19":"tag-neuroscience","20":"tag-neurotech","21":"tag-technology","22":"tag-uk","23":"tag-united-kingdom","24":"tag-upv","25":"tag-virtual-reality","26":"tag-vr"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@uk\/114310581012913135","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/6439","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/comments?post=6439"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/posts\/6439\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media\/6440"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/media?parent=6439"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/categories?post=6439"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/uk\/wp-json\/wp\/v2\/tags?post=6439"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}