Auxiliary Diagnosis of Children with Attention-Deficit/Hyperactivity Disorder: An Eye-Tracking Study with Novel Digital Biomarkers.

Abstract

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 purpose of this research 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 k-fold cross-validation.

Results: We recruited 216 participants, of whom 94 were children with ADHD and 122 were TD children. The ADHD group showed significantly poorer performance (for accuracy and completion time) than the TD group in the pro-, anti-, and delayed-saccade tasks. Additionally, there were significant group differences in digital biomarkers, such as pupil diameter fluctuation, regularity of gaze trajectory, and fixations on uninterested areas. Although the accuracy and task completion speed of the ADHD group increased over time, their eye movement patterns remained irregular. The 5-6-year-old TD group outperformed the 9-10-year-old ADHD group, 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.

Conclusions: 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.

Clinicaltrial: This study has been registrated at Chinese Clinical Trail Registry(No. ChiCTR2400087697).