Detection of autism spectrum disorder from changing of pupil diameter using multi-modal feature fusion based hybrid CNN model

Identifying Autism Gaze Patterns in Five-Second Data Records

“…The main contribution of our work is the introduction of a new metric that efficiently distinguishes children with ASD from TD children that needs only 5 s of eye-tracker data and which has a clear interpretation. This criterion provides accuracy in the range of the most recent AI models [12][13][14]. We introduce a new metric Λ, quantifying how exploratory individuals are when encountering images and which relies on only 5 s of eye-tracking data.…”

“…This metric is able to detect ASD in children with an accuracy of 93.9%, surpassing the classification accuracy of classification models using the usual metrics of fixations and saccades (accuracy 72.5%) and state-of-the-art AI classifier (89.6%). Therefore, our metric provides accuracy in the range of the most recent AI models [12][13][14], with the advantage of being understandable. We discuss how this enhanced exploratory behavior might be reflected in some parameters of the usual fixation/saccade models, namely, with faster fixations and more frequent saccades.…”

“…Regarding the classification task of gaze trajectories originating from ASD or TD children, AI algorithms have been enjoying recent success [11]. When applied to eyetracking data, these algorithms have consistently provided classification accuracy values over 90% [12][13][14]. Nonetheless, despite their remarkable success, these methods still yield caution when assisting doctors in diagnosing this condition.…”