Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study

作者：Zhongling Liu, Jinkai Li, Yuanyuan Zhang, Dan Wu, Yanyan Huo, Jianxin Yang, Musen Zhang, Chuanfei Dong, Luhui Jiang, Ruohan Sun, Ruoyin Zhou, Fei Li, Xiaodan Yu, Daqian Zhu, Yao Guo, Jinjin Chen · 发表于：JMIR mhealth and uhealth · 年份：2024 · DOI：10.2196/58927 · 被引用次数：12 · 研究领域：Attention Deficit Hyperactivity Disorder、Neural and Behavioral Psychology Studies、EEG and Brain-Computer Interfaces

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in school-aged children. The lack of objective biomarkers for ADHD often results in missed diagnoses or misdiagnoses, which lead to inappropriate or delayed interventions. Eye-tracking technology provides an objective method to assess children's neuropsychological behavior. OBJECTIVE: The aim of this study was to develop an objective and reliable auxiliary diagnostic system for ADHD using eye-tracking technology. This system would be valuable for screening for ADHD in schools and communities and may help identify objective biomarkers for the clinical diagnosis of ADHD. METHODS: We conducted a case-control study of children with ADHD and typically developing (TD) children. We designed an eye-tracking assessment paradigm based on the core cognitive deficits of ADHD and extracted various digital biomarkers that represented participant behaviors. These biomarkers and developmental patterns were compared between the ADHD and TD groups. Machine learning (ML) was implemented to validate the ability of the extracted eye-tracking biomarkers to predict ADHD. The performance of the ML models was evaluated using 5-fold cross-validation. RESULTS: We recruited 216 participants, of whom 94 (43.5%) were children with ADHD and 122 (56.5%) were TD children. The ADHD group showed significantly poorer performance (for accuracy and completion time) than the TD group in the prosaccade, antisaccade, ...