Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis

TL;DR

This paper presents an AI-based method using eye gaze-derived image transforms and transfer learning to enable faster, privacy-preserving, in-home autism diagnosis, improving accessibility and reducing costs.

Contribution

It introduces a novel diagnostic approach combining eye gaze variables with image transforms and transfer learning for efficient ASD detection.

Findings

Enables in-home autism diagnosis with privacy preservation

Reduces diagnostic time and costs

Improves communication between caregivers and therapists

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.