Graph Attention Networks for Gait-Based Autism Spectrum Disorder Detection and Interpretability

Gait analysis is an important technique for diagnosing, monitoring and rehabilitating neurological conditions such as Autism Spectrum Disorder, also known as ASD. With the increasing employment of AI in the medical domain, gait analysis allows researchers and doctors to improve their ability in modeling and interpreting complex structured data. In this delicate domain, explainable models become essential to build medical frameworks that can fasten and objectify the diagnosis procedure which is generally slow and ineffective. This study explores the use of explainability techniques applied to graph neural networks to enhance the understanding of decisions made by gait analysis models to detect ASD. The findings demonstrate that integrating explainability AI tools in this particular domain increases the accuracy of ASD detection and improves model transparency of the entire diagnosis process, allowing specialists to interpret and validate the extracted information and facilitating the adoption of these models in clinical settings.