2024/02/15 by Kleanthis Avramidis, Avramidis, Kleanthis, Melinda Y. Chang +7 · 1 citation
Computer Science · Medicine · #FOS: Electrical engineering #Gaze Tracking and Assistive Technology #Image and Video Processing (eess.IV) #Retinal Imaging and Analysis #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.09655
openalex publication_date 2024/02/15 · openalex created_date 2024/02/18 · openalex updated_date 2026/07/28
A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime example of such conditions, resulting from damage to visual pathways in the brain, and adversely impacting low- and high-level visual function. The characteristics impacted by CVI are primarily described qualitatively, challenging the establishment of an objective, evidence-based measure of CVI severity. To study those characteristics, we propose to create visual saliency maps by adequately prompting deep vision models with attributes of clinical interest. After extracting saliency maps for a curated set of stimuli, we evaluate fixation traces on those from children with CVI through eye tracking technology. Our experiments reveal significant gaze markers that verify clinical knowledge and yield nuanced discriminability when compared to those of age-matched control subjects. Using deep learning to unveil atypical visual saliency is an important step toward establishing an eye-tracking signature for severe neurodevelopmental disorders, like CVI.