AUTHOR=Chen Xiaolu , Wang Sihan , Yang Xiaowen , Yu Chunmei , Ni Fang , Yang Jie , Tian Yu , Ye Jiucai , Liu Hao , Luo Rong 

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

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2023.1260031

DOI=10.3389/fpsyt.2023.1260031

ISSN=1664-0640

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 towards target) and 14 antisaccade (looking away from target) trials, measuring attention and inhibition, respectively. 
Results: Two-way MANOVA analyses demonstrated that diagnosis and age had significant effects on performance on the fixation task (diagnosis: F(2, 432) = 8.231, p < 0.001, ***; Wilks' Λ = 0.963; age: F(2, 432) = 3.999, p < 0.019, *; Wilks' Λ = 0.982), prosaccade task (age: F(16, 418) = 3.847, p < 0.001, ***; Wilks' Λ = 0.872), and antisaccade task (diagnosis: F(16, 418) = 1.738, p = 0.038, *; Wilks' Λ = 0.938; age: F(16, 418) = 4.508, p < 0.001, ***; Wilks' Λ = 0.853). Correlational analyses revealed that participants with higher SNAP-IV score were more likely to have shorter fixation duration and more fixation intervals (r = -0.160, 95% CI [0.250, 0.067], p < 0.001, ***), poorer scores on adjusted prosaccade accuracy, and poorer scores on antisaccade accuracy (Accuracy: r = -0.105, 95% CI [-0.197, -0.011], p = 0.029, *; Adjusted accuracy: r = -0.108, 95% CI [-0.200, -0.015], p = 0.024, *).
Conclusions: Our AI-based eye tracking technology implemented on a tablet could reliably discriminate eye movements of the TD group and the ADHD group, providing a potential solution for ADHD screening outside of clinical settings.