Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Lamani, Manjunath Ramanna; Asha, V.; Padmaja, K. Title A name given to the resource Leveraging Deep Learning for Early Detection of Autism Spectrum Disorder in Augmented and Virtual Reality Mental Healthcare Environments Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Augmented and Virtual Reality in Immersive Healthcare;pp.151-173 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394302864.ch6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394302864.ch6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032078917?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032078917?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lamani M.R., Department of CSE, CHRIST University, Kanmanike, Kumbalgudu, Karnataka, Bangalore, India; Asha V., Dept of CSE, RNS Institute of Technology, Karnataka, Bengaluru, India; Padmaja K., Dept of ISE, GSSSIETW, Karnataka, Mysore, India Description An account of the resource The advent of digital health interventions offers new vistas for the early detection and management of autism spectrum disorder (ASD). This paper explores the innovative application of deep learning algorithms within augmented reality (AR) and virtual reality (VR) environments to enhance the early detection of ASD. Integrating AR and VR in mental healthcare provides immersive, controlled settings in which individuals behaviors and responses can be observed and analyzed in real time. We propose a novel framework that utilizes deep learning techniques to process and analyze the data collected from these digital environments. Our approach leverages facial expression analysis, eye tracking, and behavioral pattern recognition to identify early signs of ASD. By capturing subtle nuances in behavior often overlooked in traditional diagnostic settings, our method offers a non-invasive, engaging, and efficient alternative for early ASD detection. The potential of this technology extends beyond diagnosis, offering possibilities for personalized intervention strategies that can be adapted to each individual's unique needs. Our findings indicate that deep learning in AR and VR environments could significantly enhance the accuracy and timeliness of ASD diagnosis, paving the way for earlier and more effective interventions. This study underscores the importance of interdisciplinary collaboration in developing innovative healthcare solutions, combining the fields of artificial intelligence, psychology, and digital media to improve outcomes for individuals with ASD. 2026 Scrivener Publishing LLC. Subject The topic of the resource augmented reality; Autism spectrum disorder; deep learning; early detection; mental healthcare; virtual reality Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139430286-4; 978-139430284-0; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online