Utilizing artificial intelligence-based eye tracking technology for screening ADHD symptoms in children
Xiaolu Chen, Sihan Wang, Xiaowen Yang, Chunmei Yu, Fang Ni, Jie Yang, Yu Tian, Jiucai Ye, Hao Liu, Rong Luo
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 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). Conclusion 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.