Utilizing artificial intelligence-based eye tracking technology for screening ADHD symptoms in children.

ABSTRACT:

Objective

To explore the potential of using artificial intelligence (AI)-based eye tracking technology on a tablet for screening Attention-deficit/hyperactivity disorder (ADHD) symptoms in children.

Methods

We recruited 112 children diagnosed with ADHD (ADHD group; mean age: 9.40 ± 1.70 years old) and 325 typically developing children (TD group; mean age: 9.45 ± 1.59 years old). We designed a data-driven end-to-end convolutional neural network appearance-based model to predict eye gaze to permit eye-tracking under low resolution and sampling rates. The participants then completed the eye tracking task on a tablet, which consisted of a simple fixation task as well as 14 prosaccade (looking toward target) and 14 antisaccade (looking away from target) trials, measuring attentio

SUBMITTER: Chen X 

PROVIDER: S-EPMC10682190 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature