Abstract: Traditional diagnosis of attention deficit hyperactivity disorder (ADHD) in children is primarily through a questionnaire filled out by parents/teachers and clinical observations by doctors. It is inefficient and heavily depends on the doctor’s level of experience. In this paper, we integrate artificial intelligence (AI) technology into a software-hardware coordinated system to make ADHD diagnosis more efficient. Together with the intelligent analysis module, the camera group will collect the eye focus, facial expression, 3D body posture, and other children’s information during the completion of the functional test. Then, a multi-modal deep learning model is proposed to classify abnormal behavior fragments of children from the captured videos. In combination with other system modules, standardized diagnostic reports can be automatically generated, including test results, abnormal behavior analysis, diagnostic aid conclusions, and treatment recommendations. This system has participated in clinical diagnosis in Department of Psychology, The Children’s Hospital, Zhejiang University School of Medicine, and has been accepted and praised by doctors and patients.

Key words: Attention deficit hyperactivity disorder (ADHD), Auxiliary diagnosis, Computer vision, Deep learning, BERT

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