Abstract
The prevalence of Autism Spectrum Disorder (ASD) has surged rapidly over the past decade, posing significant challenges in communication, behavior, and focus for affected individuals. Current diagnostic techniques, though effective, are time-intensive, leading to high social and economic costs. This work introduces an AI-powered assistive technology designed to streamline ASD diagnosis and management, enhancing convenience for individuals with ASD and efficiency for caregivers and therapists. The system integrates transfer learning with image transforms derived from eye gaze variables to diagnose ASD. This facilitates and opens opportunities for in-home periodical diagnosis, reducing stress for individuals and caregivers, while also preserving user privacy through the use of image transforms. The accessibility of the proposed method also offers opportunities for improved communication between guardians and therapists, ensuring regular updates on progress and evolving support needs. Overall, the approach proposed in this work ensures timely, accessible diagnosis while protecting the subjects' privacy, improving outcomes for individuals with ASD.
Abstract (translated)
自闭症谱系障碍（ASD）的发病率在过去十年中迅速上升，给受影响个体在沟通、行为和注意力方面带来了重大挑战。尽管当前的诊断技术有效，但耗时较长，导致了较高的社会和经济成本。本文介绍了一种由人工智能驱动的辅助技术，旨在简化自闭症谱系障碍（ASD）的诊断与管理过程，提高患者的便利性，并增强照护者和治疗师的工作效率。该系统结合转移学习与基于眼动变量的图像变换来诊断 ASD，这有助于并为家庭环境中的定期诊断提供了机会，从而减轻患者及照顾者的压力，同时通过使用图像变换保护用户隐私。所提出方法的可访问性也为监护人与治疗师之间的沟通改善带来了机遇，确保了进展和不断变化的支持需求得到及时更新。总体而言，本文提出的方案保证了及时、便捷且尊重受试者隐私的诊断方式，从而提高了自闭症个体的整体结果。
URL
https://arxiv.org/abs/2506.09065
