Enhancing Autism Detection Through Gaze Analysis Using Eye Tracking Sensors and Data Attribution with Distillation in Deep Neural Networks

Autism Spectrum Disorder (ASD) is typically diagnosed through behavioral assessments, structured questionnaires, and clinician observations. While effective, these methods rely heavily on subjective interpretation, which can lead to variability in diagnostic outcomes. The integration of artificial intelligence into medical diagnostics opens up new possibilities for more precise and objective screening, especially when combined with advanced technologies such as eye tracking.

Individuals with ASD often exhibit unique gaze behaviors, such as reduced eye contact or a preference for focusing on peripheral objects rather than social stimuli like faces. Eye tracking sensors provide a powerful tool to quantify these behaviors, capturing precise information about how a person scans and fixates on different elements in a visual scene. By leveraging deep neural networks, these gaze patterns can be analyzed in real-time, identifying characteristics that distinguish individuals with ASD from neurotypical individuals.

Processing large datasets for AI training can be computationally expensive and time-intensive. To enhance efficiency, an innovative approach known as data attribution is used, allowing AI models to prioritize the most relevant training samples while filtering out noisy or misleading data. By applying a technique called TracIn, researchers can evaluate how each data point influences the model’s learning process, refining the dataset without compromising accuracy. In fact, results show that even when trained on just 77% of the dataset, the model maintained a classification accuracy of 94.35%, surpassing benchmarks and proving that selecting high-quality data is more effective than simply increasing the dataset size.

This technology has the potential to transform autism screening and diagnosis. AI-powered gaze analysis could be implemented in clinical settings, providing clinicians with an additional, objective tool to support early detection. It could also be integrated into portable diagnostic devices, making autism screening more accessible in schools or pediatric clinics. Moreover, by identifying the most influential gaze patterns linked to ASD, this research enhances the broader understanding of visual attention differences, contributing to improved therapeutic approaches.

The combination of AI, deep learning, and eye tracking represents a major step toward more reliable and interpretable medical AI applications. By improving accuracy while reducing computational overhead, this approach not only refines ASD classification but also lays the groundwork for integrating AI-driven insights into clinical practice. In the near future, these models could be adapted for other neurodevelopmental conditions, further bridging the gap between AI innovation and healthcare.