用眼动变量生成图像变换，辅助自闭症早期筛查
Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
- 基于眼动数据生成图像变换，结合迁移学习诊断自闭症
- 支持居家定期检测，提升诊断效率与隐私保护
- 适合临床辅助、家庭监测及远程康复管理
自闭症谱系障碍（ASD）发病率在过去十年急剧上升，给患者在沟通、行为和专注力方面带来显著挑战。现有诊断方法虽有效，但耗时长，导致高昂的社会与经济成本。本文提出一种AI辅助技术，通过融合迁移学习与基于眼动变量的图像变换，实现自闭症的快速诊断与管理，提升自闭症患者及照护者便利性。该系统支持居家周期性检测，减轻患者与照护者压力，同时通过图像变换保障用户隐私。方法还促进监护人与治疗师间的持续沟通，确保进度更新与支持需求动态调整。整体方案实现了及时、可及且隐私保护的诊断，改善自闭症个体干预效果。
原文摘要 · Abstract (English)
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.
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