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.

儿童注意力缺陷多动障碍（ADHD）的传统诊断主要是通过父母/老师填写的问卷以及医生的临床观察来进行的。它效率低下，在很大程度上取决于医生的经验水平。在本文中，我们将人工智能（AI）技术集成到软硬件协调系统中，以提高ADHD诊断的效率。相机组与智能分析模块一起，将在功能测试完成时收集眼睛的焦点，面部表情，3D身体姿势和其他儿童信息。然后，提出了一种多模式深度学习模型，用于从捕获的视频中对儿童的异常行为片段进行分类。结合其他系统模块，可以自动生成标准化的诊断报告，包括测试结果，异常行为分析，诊断辅助结论和治疗建议。该系统已参与浙江大学医学院附属儿童医院心理系的临床诊断，并得到医生和患者的好评。