Auxiliary diagnostic method for children with autism spectrum disorder based on virtual reality and eye-tracking technology.

Researchers developed a new way to help diagnose autism in children using virtual reality headsets that track where children look. The system achieved 85.88% accuracy in identifying autism-related eye movement patterns during emotional tasks. This technology could make autism diagnosis more objective and less dependent on individual clinician judgment, though the study doesn't report how many children were tested.

This study developed a novel diagnostic framework combining virtual reality (VR) and eye-tracking technology to assess children with autism spectrum disorder (ASD). The researchers created a gaze estimation model that integrates head and eye movement data with a lightweight Transformer architecture to analyze eye movement patterns. The system was tested using an emotion recognition task in a WebVR environment, achieving 85.88% accuracy in identifying abnormal gaze patterns associated with ASD. The approach aims to reduce clinician subjectivity and improve diagnostic objectivity by providing precise behavioral analysis tools that address ASD-related challenges such as attention instability and emotional variability during social interactions.

This technology represents a promising advancement toward objective autism diagnosis, potentially reducing clinician bias and improving consistency. However, extensive validation studies with larger samples and comparison to established diagnostic tools are needed before clinical implementation. The approach may eventually supplement traditional assessment methods.

Sample size not reported, limiting generalizability. Single study without comparison to established diagnostic methods. No validation against gold-standard autism assessments. Unclear participant characteristics, age ranges, or diagnostic criteria used. Technology accessibility and implementation feasibility not addressed.

In the behavioral analysis of children with Autism Spectrum Disorder (ASD), virtual reality (VR)-based eye-tracking technology offers a precise method for assessing social and cognitive characteristics. It overcomes the limitations of traditional diagnostic methods, such as clinician subjectivity and experience bias. VR also addresses ASD-related challenges like attention instability and emotional variability during social interactions. This paper combines eye-tracking with VR environments to analyze gaze patterns in children with ASD.

It proposes a new diagnostic framework to improve objectivity and accuracy.The gaze estimation model integrates head and eye movement data to predict gaze direction. It enhances precision using binocular fusion and employs multi-scale convolutional kernels to extract hierarchical eye movement features. The model simplifies network connections to retain essential information. A lightweight Transformer architecture models long-range temporal dependencies in eye movements.

A Bayesian decision model is used to classify fixations, saccades, and smooth pursuit.To test the model, an emotion recognition task was designed in a WebVR environment. Gaze data from children with ASD were collected, key features were extracted, and abnormal patterns were identified for diagnostic support. The experimental results showed an 85.88% accuracy rate. This confirms the effectiveness of combining VR and eye-tracking technology in ASD diagnosis, advancing intelligent medical tools, and reducing reliance on subjective clinical judgment.