基于人工智能技术的儿童ADHD辅助诊断系统

张雁翼; 孔鸣; 赵天琦; 洪文琛; 谢迪; 王春茂; 杨荣旺; 李荣; 朱强

doi:10.1631/FITEE.1900729

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基于人工智能技术的儿童ADHD辅助诊断系统

常规文章 | Updated：2022-05-19

- 基于人工智能技术的儿童ADHD辅助诊断系统
- Auxiliary diagnostic system for ADHD in children based on AI technology
- 信息与电子工程前沿（英文） 2021年22卷第3期页码：400-414
- Affiliations：
  
  Department of Psychology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou 310052, China
  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
  Hikvision Research Institute, Hangzhou 310052, China
- Author bio：
  
  [ "Yanyi ZHANG, E-mail: doczyy1981@sina.com" ]
  [ "Ming KONG, E-mail: zjukongming@zju.edu.cn" ]
  Qiang ZHU, E-mail: zhuq@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61625107)
- DOI：10.1631/FITEE.1900729
  中图分类号： TP391.4
- 收稿：2019-12-25，
  
  修回：2020-11-13，
  
  纸质出版：2021-03
- Accepted：
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张雁翼, 孔鸣, 赵天琦, 等. 基于人工智能技术的儿童ADHD辅助诊断系统[J]. 信息与电子工程前沿（英文）, 2021,22(3):400-414.

Yanyi ZHANG, Ming KONG, Tianqi ZHAO, et al. Auxiliary diagnostic system for ADHD in children based on AI technology[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(3): 400-414.
张雁翼, 孔鸣, 赵天琦, 等. 基于人工智能技术的儿童ADHD辅助诊断系统[J]. 信息与电子工程前沿（英文）, 2021,22(3):400-414. DOI： 10.1631/FITEE.1900729.

Yanyi ZHANG, Ming KONG, Tianqi ZHAO, et al. Auxiliary diagnostic system for ADHD in children based on AI technology[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(3): 400-414. DOI： 10.1631/FITEE.1900729.

摘要

传统的儿童注意缺陷多动障碍（ADHD）诊断主要基于由父母/老师填写的调查问卷和医生的临床观察，不仅效率不高，而且诊断准确率很大程度上取决于医生的经验水平。本文将人工智能技术结合到一种软硬件协同辅助诊断系统中，以使ADHD诊断更为高效。通过集成智能分析模块，相机模组将采集受试儿童完成执行功能测试时的眼部注意力、面部表情、3D身体姿态和其他测试信息。然后，提出一种多模态深度学习模型，用于对所采集视频中儿童的异常行为片段进行分类。结合其他系统模块所采集的信息，辅助诊断系统能够自动生成标准化的诊断报告，包括测试结果、异常行为分析、辅助诊断结论和治疗建议。该系统目前实际部署在浙江大学医学院附属儿童医院心理科，用于临床辅助诊断，得到医生和患者一致好评。

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.

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Keywords

references

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