Full Text:   <634>

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CLC number: TP391.4

On-line Access: 2021-03-08

Received: 2019-12-25

Revision Accepted: 2020-06-27

Crosschecked: 2020-11-13

Cited: 0

Clicked: 1396

Citations:  Bibtex RefMan EndNote GB/T7714


Yanyi Zhang


Ming Kong


Qiang Zhu


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.3 P.400-414


Auxiliary diagnostic system for ADHD in children based on AI technology

Author(s):  Yanyi Zhang, Ming Kong, Tianqi Zhao, Wenchen Hong, Di Xie, Chunmao Wang, Rongwang Yang, Rong Li, Qiang Zhu

Affiliation(s):  Department of Psychology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China; more

Corresponding email(s):   doczyy1981@sina.com, zjukongming@zju.edu.cn, zhuq@zju.edu.cn

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

Yanyi Zhang, Ming Kong, Tianqi Zhao, Wenchen Hong, Di Xie, Chunmao Wang, Rongwang Yang, Rong Li, Qiang Zhu. Auxiliary diagnostic system for ADHD in children based on AI technology[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(3): 400-414.

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A1 - Tianqi Zhao
A1 - Wenchen Hong
A1 - Di Xie
A1 - Chunmao Wang
A1 - Rongwang Yang
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A1 - Qiang Zhu
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DOI - 10.1631/FITEE.1900729

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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