Current ADHD diagnostic practices rely on subjective rating scales and continuous performance tests with limited specificity. We propose an objective deep-learning approach classifying ADHD via task-evoked pupil diameter and binocular eye-movement synchrony during a visual cueing task in 439 participants across 14 clinical centers. We implemented two independent models: a multiple instance learning (MIL) framework for pupil dynamics and conventional classifiers for eye-movement synchrony. The…
