Autism Detection with Technology-driven Gaze Tracking

Abstract

Early detection and rehabilitation for Autism Spectrum Disorder (ASD) can be challenging, hampering optimal outcomes. Despite breakthroughs in research, the nuanced nature of ASD symptoms frequently leads to delayed diagnoses, compromising the effectiveness of interventions. Furthermore, inadequate access to specialist care exacerbates the difficulties encountered by both families and healthcare practitioners. The research explores how eye-gaze tracking using webcam and machine learning can detect autism in children, and it also assesses how DeepAssist Vision uses these advanced technology to scan and interpret gaze patterns, providing vital insights into social communication deficits in ASD patients. It will also highlight the importance of the integration of machine learning techniques, which allows the system to learn and adapt to a wide range of individual behaviors, improving its ability to detect suspected autism symptoms. Finally, the article suggests ways to improve technology-driven gaze monitoring for autism identification. DeepAssist Vision enables continual development through algorithm refinement, dataset expansion, and real-time monitoring. The emphasis turns to providing user-friendly interfaces, assuring ubiquitous accessibility, and fostering collaboration among healthcare providers, technologists, and caretakers. The study underlines the importance of continued research to improve the efficiency, accuracy, and ethical issues around the application of such novel technology in autism detection and intervention.