Yoo Jae Hyun, Kang ChangSu, Lim Joon Shik, Wang Bohyun, Choi Chi-Hyun, Hwang Hyunchan, Han Doug Hyun, Kim Hyungjun, Cheon Hosang, Kim Jae-Won
Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
Department of Computer Science, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea.
Front Psychiatry. 2024 Feb 15;15:1337595. doi: 10.3389/fpsyt.2024.1337595. eCollection 2024.
Attention-deficit/hyperactivity disorder (ADHD) affects a significant proportion of the pediatric population, making early detection crucial for effective intervention. Eye movements are controlled by brain regions associated with neuropsychological functions, such as selective attention, response inhibition, and working memory, and their deficits are related to the core characteristics of ADHD. Herein, we aimed to develop a screening model for ADHD using machine learning (ML) and eye-tracking features from tasks that reflect neuropsychological deficits in ADHD.
Fifty-six children (mean age 8.38 ± 1.58, 45 males) diagnosed with ADHD based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition were recruited along with seventy-nine typically developing children (TDC) (mean age 8.80 ± 1.82, 33 males). Eye-tracking data were collected using a digital device during the performance of five behavioral tasks measuring selective attention, working memory, and response inhibition (pro-saccade task, anti-saccade task, memory-guided saccade task, change detection task, and Stroop task). ML was employed to select relevant eye-tracking features for ADHD, and to subsequently construct an optimal model classifying ADHD from TDC.
We identified 33 eye-tracking features in the five tasks with the potential to distinguish children with ADHD from TDC. Participants with ADHD showed increased saccade latency and degree, and shorter fixation time in eye-tracking tasks. A soft voting model integrating extra tree and random forest classifiers demonstrated high accuracy (76.3%) at identifying ADHD using eye-tracking features alone. A comparison of the model using only eye-tracking features with models using the Advanced Test of Attention or Stroop test showed no significant difference in the area under the curve (AUC) (p = 0.419 and p=0.235, respectively). Combining demographic, behavioral, and clinical data with eye-tracking features improved accuracy, but did not significantly alter the AUC (p=0.208).
Our study suggests that eye-tracking features hold promise as ADHD screening tools, even when obtained using a simple digital device. The current findings emphasize that eye-tracking features could be reliable indicators of impaired neurobiological functioning in individuals with ADHD. To enhance utility as a screening tool, future research should be conducted with a larger sample of participants with a more balanced gender ratio.
注意缺陷多动障碍（ADHD）影响着相当一部分儿童群体，因此早期发现对于有效干预至关重要。眼球运动由与神经心理功能相关的脑区控制，如选择性注意、反应抑制和工作记忆，而这些功能的缺陷与ADHD的核心特征相关。在此，我们旨在利用机器学习（ML）和反映ADHD神经心理缺陷的任务中的眼动追踪特征，开发一种ADHD筛查模型。
招募了56名根据《精神疾病诊断与统计手册》第五版诊断为ADHD的儿童（平均年龄8.38±1.58岁，45名男性）以及79名发育正常的儿童（TDC）（平均年龄8.80±1.82岁，33名男性）。在执行五项测量选择性注意、工作记忆和反应抑制的行为任务（顺向扫视任务、逆向扫视任务、记忆引导扫视任务、变化检测任务和Stroop任务）期间，使用数字设备收集眼动追踪数据。采用ML选择与ADHD相关的眼动追踪特征，并随后构建一个将ADHD与TDC分类的最优模型。
我们在五项任务中识别出33个眼动追踪特征，这些特征有可能区分ADHD儿童和TDC。ADHD参与者在眼动追踪任务中表现出扫视潜伏期和幅度增加，注视时间缩短。一个整合了极端随机树和随机森林分类器的软投票模型在仅使用眼动追踪特征识别ADHD方面表现出较高的准确率（76.3%）。将仅使用眼动追踪特征的模型与使用注意力高级测试或Stroop测试的模型进行比较，曲线下面积（AUC）无显著差异（分别为p = 0.419和p = 0.235）。将人口统计学、行为学和临床数据与眼动追踪特征相结合提高了准确率，但未显著改变AUC（p = 0.208）。
我们的研究表明，即使使用简单的数字设备获取，眼动追踪特征也有望成为ADHD筛查工具。当前研究结果强调，眼动追踪特征可能是ADHD个体神经生物学功能受损的可靠指标。为了提高作为筛查工具的效用，未来研究应使用更大样本且性别比例更均衡的参与者进行。
