A multidimensional eye-tracking assessment for estimating cognitive profiles in intellectual disability: A preliminary deep learning study.

Objective: The current diagnosis of intellectual disability (ID) in children relies on resource-intensive assessments by experts, limiting their use for widespread estimation. Eye-tracking offers a potential digital biomarker, but its application to the multifaceted cognitive profile of ID remains scarce. This study aimed to develop and validate a novel eye-tracking assessment combined with deep learning as an automated tool for estimating cognitive capacity of ID. Methods: We developed three cognitive subtasks to elicit spatio-temporal gaze patterns related to three subindices including verbal comprehension (VCI), fluid reasoning (FRI), and working memory (WMI). With data collected from seven children with ID and nine typically developing (TD) children, we compared a logistic regression (LR) model using predefined gaze metrics and behavioral features with a convolutional neural network (CNN) trained directly on raw scanpath images to classify participants. Results: The CNN model demonstrated superior performance, achieving a 0.93 F1-score in subject-level classification, while the feature-based LR model achieved a 0.76 F1-score. Notably, the CNN predictions derived from the working memory task significantly correlated with full-scale IQ as well as FRI and visuospatial (VSI) subscores, suggesting the model effectively captured higher-order reasoning and visuospatial processes. Conclusions: This study demonstrates that deep learning analysis of spatio-temporal gaze patterns from a multidimensional cognitive task can serve as a robust digital biomarker, paving the way for accessible and objective tools for estimating cognitive capacity in children with neurodevelopmental disorders.