ADHD Intelligent Auxiliary Diagnosis System Based on Multimodal Information Fusion

The traditional medical diagnosis methods of ADHD mainly rely on scale evaluation and interview observation. The diagnosis conclusion is subjective and extremely dependent on the doctor's experience level. There is an urgent need to improve diagnosis efficiency and improve the diagnosis standard through other technical means in the clinical process. We have designed and developed the ADHD intelligent auxiliary diagnosis system with software and hardware cooperation. The system performs a set of functional test tasks, uses a camera module to capture multimodal information such as facial expressions, eye movements, limb movements, language expressions and reaction abilities of children during task completion, and uses computer vision technology to automatically extract measurable characteristics. Finally, deep learning technology is used to detect children's specific behaviors in the video, which is complementary to the existing doctor's diagnosis basis. This system was deployed in the Department of Psychology of Children's Hospital of Zhejiang University in July 2019 and has been used in actual clinical diagnosis to date. It has completed the testing and evaluation of hundreds of ADHD children.