Auxiliary diagnostic system for ADHD in children based on AI technology

Yanyi ZHANG; Ming KONG; Tianqi ZHAO; Wenchen HONG; Di XIE; Chunmao WANG; Rongwang YANG; Rong LI; Qiang ZHU

doi:10.1631/FITEE.1900729

Your Location：

Home >

Browse articles >

Auxiliary diagnostic system for ADHD in children based on AI technology

Regular Papers | Updated：2022-05-19

- Auxiliary diagnostic system for ADHD in children based on AI technology
- 基于人工智能技术的儿童ADHD辅助诊断系统
- Frontiers of Information Technology & Electronic Engineering Vol. 22, Issue 3, Pages: 400-414(2021)
- 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：
- DOI：10.1631/FITEE.1900729
  CLC： TP391.4
- Received：25 December 2019，
  
  Revised：2020-11-13，
  
  Published：2021-03
- Accepted：
Scan QR Code
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：

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.

关键词

Keywords

references

AMS Aradhya , , , A Joglekar , , , S Suresh , , , 等 . . Deep transformation method for discriminant analysis of multi-channel resting state fMRI . . Proc AAAI Conf on Artificial Intelligence , , 2019 . . p.2556 - - 2563 . . DOI: 10.1609/aaai.v33i01.33012556 http://doi.org/10.1609/aaai.v33i01.33012556 . .

MS Atkins , , , WE Pelham , , , MH Licht . . A comparison of objective classroom measures and teacher ratings of attention deficit disorder . . J Abnorm Child Psychol , , 1985 . . 13 ( ( 1 ): ): 155 - - 167 . . DOI: 10.1007/BF00918379 http://doi.org/10.1007/BF00918379 . .

T Baltrušaitis , , , P Robinson , , , LP Morency . . Continuous conditional neural fields for structured regression . . Proc 13 th European Conf on Computer Vision , , 2014 . . p. 593 - - 608 . . DOI: 10.1007/978-3-319-10593-2_39 http://doi.org/10.1007/978-3-319-10593-2_39 . .

CJ Bench , , , CD Frith , , , PM Grasby , , , 等 . . Investigations of the functional anatomy of attention using the Stroop test . . Neuropsychologia , , 1993 . . 31 ( ( 9 ): ): 907 - - 922 . . DOI: 10.1016/0028-3932(93)90147-R http://doi.org/10.1016/0028-3932(93)90147-R . .

P Birleson , , , I Hudson , , , DG Buchanan , , , 等 . . Clinical evaluation of a self-rating scale for depressive disorder in childhood (depression self-rating scale) . . J Child Psychol Psych , , 1987 . . 28 ( ( 1 ): ): 43 - - 60 . . DOI: 10.1111/j.1469-7610.1987.tb00651.x http://doi.org/10.1111/j.1469-7610.1987.tb00651.x . .

B Birmaher , , , S Khetarpal , , , D Brent , , , 等 . . The screen for child anxiety related emotional disorders (SCARED): scale construction and psychometric characteristics . . J Am Acad Child Adolesc Psych , , 1997 . . 36 ( ( 4 ): ): 545 - - 553 . . DOI: 10.1097/00004583-199704000-00018 http://doi.org/10.1097/00004583-199704000-00018 . .

Z Cao , , , T Simon , , , SE Wei , , , 等 . . Realtime multi-person 2D pose estimation using part affinity fields . . Proc IEEE Conf on Computer Vision and Pattern Recognition , , 2017 . . p. 7291 - - 7299 . . DOI: 10.1109/CVPR.2017.143 http://doi.org/10.1109/CVPR.2017.143 . .

M Chen , , , HL Li , , , JH Wang , , , 等 . . A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection . . Radiol Artif Intell , , 2019 . . 2 ( ( 1 ): ): e190012 DOI: 10.1148/ryai.2019190012 http://doi.org/10.1148/ryai.2019190012 . .

CK Conners , , , J Pitkanen , , , SR Rzepa . . Conners comprehensive behavior rating scale. In: Kreutzer JS, DeLuca J, Caplan B (Eds. ), Encyclopedia of Clinical Neuropsychology , , : : New York, USA Springer , , 2011 . . p. 678 - - 680 . . DOI: 10.1007/978-0-387-79948-3_1536 http://doi.org/10.1007/978-0-387-79948-3_1536 . .

J Devlin , , , MW Chang , , , K Lee , , , 等 . . BERT: pre-training of deep bidirectional transformers for language understanding . . Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , , 2019 . . p.4171 - - 4186 . . DOI: 10.18653/v1/N19-1423 http://doi.org/10.18653/v1/N19-1423 . .

P Ekman . . Basic emotions. In: Dalgleish T, Dalgleish MJ (Eds. ), Handbook of Cognition and Emotion , , : : New York, USA Wiley , , 1999 . . p. 301 - - 320 . . DOI: 10.1002/0470013494.ch3 http://doi.org/10.1002/0470013494.ch3 . .

A Graves , , , J Schmidhuber . . Framewise phoneme classification with bidirectional LSTM and other neural network architectures . . Neur Netw , , 2005 . . 18 ( ( 5-6 ): ): 602 - - 610 . . DOI: 10.1016/j.neunet.2005.06.042 http://doi.org/10.1016/j.neunet.2005.06.042 . .

J Hamm , , , CG Kohler , , , RC Gur , , , 等 . . Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders . . J Neurosci Meth , , 2011 . . 200 ( ( 2 ): ): 237 - - 256 . . DOI: 10.1016/j.jneumeth.2011.06.023 http://doi.org/10.1016/j.jneumeth.2011.06.023 . .

S Jaiswal , , , MF Valstar , , , A Gillott , , , 等 . . Automatic detection of ADHD and ASD from expressive behaviour in RGBD data . . Proc 12 th IEEE Int Conf on Automatic Face & Gesture Recognition , , 2017 . . p. 762 - - 769 . . DOI: 10.1109/FG.2017.95 http://doi.org/10.1109/FG.2017.95 . .

DE King . . Dlib-ml: a machine learning toolkit . . J Mach Learn Res , , 2009 . . 10 1755 - - 1758 . . . .

HW Kuhn . . The Hungarian method for the assignment problem . . Nav Res Logist Q , , 1955 . . 2 ( ( 1-2 ): ): 83 - - 97 . . DOI: 10.1002/nav.3800020109 http://doi.org/10.1002/nav.3800020109 . .

M Leo , , , P Carcagnì , , , C Distante , , , 等 . . Computational assessment of facial expression production in ASD children . . Sensors , , 2018 . . 18 ( ( 11 ): ): 3993 DOI: 10.3390/s18113993 http://doi.org/10.3390/s18113993 . .

V Lepetit , , , F Moreno-Noguer , , , P Fua . . EP n P: an accurate O ( n ) solution to the P n P problem . . Int J Comput Vis , , 2009 . . 81 ( ( 2 ): ): 155 DOI: 10.1007/s11263-008-0152-6 http://doi.org/10.1007/s11263-008-0152-6 . .

J Li , , , YH Zhong , , , JX Han , , , 等 . . Classifying ASD children with LSTM based on raw videos . . Neurocomputing , , 2019 . . 390 226 - - 238 . . DOI: 10.1016/j.neucom.2019.05.106 http://doi.org/10.1016/j.neucom.2019.05.106 . .

JL Marcano , , , MA Bell , , , AAL Beex . . Classification of ADHD and non-ADHD subjects using a universal background model . . Biomed Signal Process Contr , , 2018 . . 39 204 - - 212 . . DOI: 10.1016/j.bspc.2017.07.023 http://doi.org/10.1016/j.bspc.2017.07.023 . .

J Martinez , , , R Hossain , , , J Romero , , , 等 . . A simple yet effective baseline for 3d human pose estimation . . Proc IEEE Int Conf on Computer Vision , , 2017 . . p. 2640 - - 2649 . . DOI: 10.1109/ICCV.2017.288 http://doi.org/10.1109/ICCV.2017.288 . .

O Monchi , , , M Petrides , , , V Petre , , , 等 . . Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging . . J Neurosci , , 2001 . . 21 ( ( 19 ): ): 7733 - - 7741 . . DOI: 10.1523/JNEUROSCI.21-19-07733.2001 http://doi.org/10.1523/JNEUROSCI.21-19-07733.2001 . .

M Muñoz-Organero , , , L Powell , , , B Heller , , , 等 . . Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle . . Sensors , , 2019 . . 19 ( ( 13 ): ): 2935 DOI: 10.3390/s19132935 http://doi.org/10.3390/s19132935 . .

AM Oerlemans , , , JM van der Meer , , , DJ van Steijn , , , 等 . . Recognition of facial emotion and affective prosody in children with ASD (+ADHD) and their unaffected siblings . . Eur Child Adolesc Psych , , 2014 . . 23 ( ( 5 ): ): 257 - - 271 . . DOI: 10.1007/s00787-013-0446-2 http://doi.org/10.1007/s00787-013-0446-2 . .

GV Polanczyk , , , EG Willcutt , , , GA Salum , , , 等 . . ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis . . Int J Epidemiol , , 2014 . . 43 ( ( 2 ): ): 434 - - 442 . . DOI: 10.1093/ije/dyt261 http://doi.org/10.1093/ije/dyt261 . .

JC Raven , , , JH Court , , , J Raven . . Manual for Raven's Progressive Matrices and Vocabulary Scales: Standard Progressive Matrices , , : : London, UK Lewis, , , 1983 . . .

K Sayal , , , V Prasad , , , D Daley , , , 等 . . ADHD in children and young people: prevalence, care pathways, and service provision . . Lancet Psych , , 2018 . . 5 ( ( 2 ): ): 175 - - 186 . . DOI: 10.1016/S2215-0366(17)30167-0 http://doi.org/10.1016/S2215-0366(17)30167-0 . .

CF Saylor , , , AJ Finch , , , A Spirito , , , 等 . . The children's depression inventory: a systematic evaluation of psychometric properties . . J Consult Clin Psychol , , 1984 . . 52 ( ( 6 ): ): 955 - - 967 . . DOI: 10.1037/0022-006X.52.6.955 http://doi.org/10.1037/0022-006X.52.6.955 . .

K Simonyan , , , A Zisserman . . Very deep convolutional networks for large-scale image recognition , , 2014 . . https://arxiv.org/abs/1409.1556 https://arxiv.org/abs/1409.1556 , , . .

T Thompson , , , A Lloyd , , , A Joseph , , , 等 . . The Weiss functional impairment rating scale-parent form for assessing ADHD: evaluating diagnostic accuracy and determining optimal thresholds using ROC analysis . . Qual Life Res , , 2017 . . 26 ( ( 7 ): ): 1879 - - 1885 . . DOI: 10.1007/s11136-017-1514-8 http://doi.org/10.1007/s11136-017-1514-8 . .

A Vaswani , , , N Shazeer , , , N Parmar , , , 等 . . Attention is all you need . . Proc 31 st Int Conf on Neural Information Processing Systems , , 2017 . . p. 5998 - - 6008 . . . .

TT Wang , , , KH Liu , , , ZZ Li , , , 等 . . Prevalence of attention deficit/hyperactivity disorder among children and adolescents in China: a systematic review and meta-analysis . . BMC Psych , , 2017 . . 17 ( ( 1 ): ): 32 DOI: 10.1186/s12888-016-1187-9 http://doi.org/10.1186/s12888-016-1187-9 . .

EG Willcutt , , , JT Nigg , , , BF Pennington , , , 等 . . Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes . . J Abnorm Psychol , , 2012 . . 121 ( ( 4 ): ): 991 - - 1010 . . DOI: 10.1037/a0027347 http://doi.org/10.1037/a0027347 . .

L Zou , , , JN Zheng , , , CY Miao , , , 等 . . 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI . . IEEE Access , , 2017 . . 5 23626 - - 23636 . . DOI: 10.1109/ACCESS.2017.2762703 http://doi.org/10.1109/ACCESS.2017.2762703 . .

Views

152

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

MltAuxTSPP: a unified benchmark for deep learning-based traffic state prediction with multi-source auxiliary data

Q-space-coordinate-guided neural networks for high-fidelity diffusion tensor estimation from minimal diffusion-weighted images

CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection

Federated model with contrastive learning and adaptive control variates for human activity recognition

A comprehensive survey of physical adversarial vulnerabilities in autonomous driving systems

Related Author

Yusong ZHOU

Xiaoyu JIANG

Shu SUN

Xinmin ZHANG

Yuanqiu MO

Zhihuan SONG

Maokun ZHENG

Zhi LI

Related Institution

School of Mathematics, Southeast University

State Key Laboratory of Industrial Control Technology, Zhejiang University

College of Control Science and Engineering, Zhejiang University

State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰