Child Health Care Medical Division, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
JMIR Mhealth Uhealth. 2024 Nov 29;12:e58927. doi: 10.2196/58927.
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.
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.
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.
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, and delayed saccade tasks. In addition, there were substantial group differences in digital biomarkers, such as pupil diameter fluctuation, regularity of gaze trajectory, and fixations on unrelated areas. Although the accuracy and task completion speed of the ADHD group increased over time, their eye-movement patterns remained irregular. The TD group with children aged 5 to 6 years outperformed the ADHD group with children aged 9 to 10 years, and this difference remained relatively stable over time, which indicated that the ADHD group followed a unique developmental pattern. The ML model was effective in discriminating the groups, achieving an area under the curve of 0.965 and an accuracy of 0.908.
The eye-tracking biomarkers proposed in this study effectively identified differences in various aspects of eye-movement patterns between the ADHD and TD groups. In addition, the ML model constructed using these digital biomarkers achieved high accuracy and reliability in identifying ADHD. Our system can facilitate early screening for ADHD in schools and communities and provide clinicians with objective biomarkers as a reference.
注意力缺陷多动障碍（ADHD）是学龄儿童常见的神经发育障碍。ADHD 缺乏客观的生物标志物，往往导致漏诊或误诊，从而导致干预不当或延迟。眼动跟踪技术提供了一种评估儿童神经心理行为的客观方法。
本研究旨在利用眼动跟踪技术开发一种用于 ADHD 的客观、可靠的辅助诊断系统。该系统可用于学校和社区的 ADHD 筛查，并有助于识别 ADHD 临床诊断的客观生物标志物。
我们对 ADHD 患儿和典型发育（TD）儿童进行了病例对照研究。我们根据 ADHD 的核心认知缺陷设计了眼动跟踪评估范式，并提取了代表参与者行为的各种数字生物标志物。比较了 ADHD 组和 TD 组之间的这些生物标志物和发育模式。实施机器学习（ML）以验证提取的眼动跟踪生物标志物预测 ADHD 的能力。使用 5 折交叉验证评估 ML 模型的性能。
我们共招募了 216 名参与者，其中 94 名（43.5%）为 ADHD 患儿，122 名（56.5%）为 TD 儿童。ADHD 组在直眼追踪、反眼追踪和延迟眼追踪任务中的准确性和完成时间明显差于 TD 组。此外，在瞳孔直径波动、注视轨迹规则性和对无关区域的注视等数字生物标志物方面存在显著的组间差异。尽管 ADHD 组的准确性和任务完成速度随时间增加，但他们的眼动模式仍然不规则。5 至 6 岁的 TD 儿童组的表现优于 9 至 10 岁的 ADHD 儿童组，并且这种差异随时间相对稳定，这表明 ADHD 组遵循独特的发育模式。ML 模型在区分两组方面效果显著，曲线下面积为 0.965，准确率为 0.908。
本研究提出的眼动跟踪生物标志物有效识别了 ADHD 组和 TD 组在眼动模式各方面的差异。此外，使用这些数字生物标志物构建的 ML 模型在识别 ADHD 方面具有较高的准确性和可靠性。我们的系统可以促进学校和社区中 ADHD 的早期筛查，并为临床医生提供客观的生物标志物作为参考。
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