Full Text:   <634>

Summary:  <574>

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

 ORCID:

Yanyi Zhang

https://orcid.org/0000-0001-5238-1712

Ming Kong

https://orcid.org/0000-0002-6177-3707

Qiang Zhu

https://orcid.org/0000-0002-2405-6776

-   Go to

Article info.
Open peer comments

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

http://doi.org/10.1631/FITEE.1900729


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.

@article{title="Auxiliary diagnostic system for ADHD in children based on AI technology",
author="Yanyi Zhang, Ming Kong, Tianqi Zhao, Wenchen Hong, Di Xie, Chunmao Wang, Rongwang Yang, Rong Li, Qiang Zhu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="3",
pages="400-414",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900729"
}

%0 Journal Article
%T Auxiliary diagnostic system for ADHD in children based on AI technology
%A Yanyi Zhang
%A Ming Kong
%A Tianqi Zhao
%A Wenchen Hong
%A Di Xie
%A Chunmao Wang
%A Rongwang Yang
%A Rong Li
%A Qiang Zhu
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 3
%P 400-414
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900729

TY - JOUR
T1 - Auxiliary diagnostic system for ADHD in children based on AI technology
A1 - Yanyi Zhang
A1 - Ming Kong
A1 - Tianqi Zhao
A1 - Wenchen Hong
A1 - Di Xie
A1 - Chunmao Wang
A1 - Rongwang Yang
A1 - Rong Li
A1 - Qiang Zhu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 3
SP - 400
EP - 414
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900729


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.

基于人工智能技术的儿童ADHD辅助诊断系统

张雁翼1,孔鸣2,赵天琦2,洪文琛2,谢迪3,王春茂3,杨荣旺1,李荣1,朱强2
1浙江大学医学院附属儿童医院(国家儿童健康与疾病临床医学研究中心)心理科,中国杭州市,310052
2浙江大学计算机科学与技术学院,中国杭州市,310027
3海康威视研究院,中国杭州市,310052
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。

关键词:注意缺陷多动障碍(ADHD);辅助诊断;计算机视觉;深度学习;BERT

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

Reference

[1]Aradhya AMS, Joglekar A, Suresh S, et al., 2019. Deep transformation method for discriminant analysis of multi-channel resting state fMRI. Proc AAAI Conf on Artificial Intelligence, p.2556-2563.

[2]Atkins MS, Pelham WE, Licht MH, 1985. A comparison of objective classroom measures and teacher ratings of attention deficit disorder. J Abnorm Child Psychol, 13(1):155-167.

[3]Baltruv{s}aitis T, Robinson P, Morency LP, 2014. Continuous conditional neural fields for structured regression. Proc 13th European Conf on Computer Vision, p.593-608.

[4]Bench CJ, Frith CD, Grasby PM, et al., 1993. Investigations of the functional anatomy of attention using the Stroop test. Neuropsychologia, 31(9):907-922.

[5]Birleson P, Hudson I, Buchanan DG, et al., 1987. Clinical evaluation of a self-rating scale for depressive disorder in childhood (depression self-rating scale). J Child Psychol Psych, 28(1):43-60.

[6]Birmaher B, Khetarpal S, Brent D, et al., 1997. The screen for child anxiety related emotional disorders (SCARED): scale construction and psychometric characteristics. J Am Acad Child Adolesc Psych, 36(4):545-553.

[7]Cao Z, Simon T, Wei SE, et al., 2017. Realtime multi-person 2D pose estimation using part affinity fields. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.7291-7299.

[8]Chen M, Li HL, Wang JH, et al., 2019. A multichannel deep neural network model analyzing multiscale functional brain connectome data for attention deficit hyperactivity disorder detection. Radiol Artif Intell, 2(1):e190012.

[9]Conners CK, Pitkanen J, Rzepa SR, 2011. Conners comprehensive behavior rating scale. In: Kreutzer JS, DeLuca J, Caplan B (Eds.), Encyclopedia of Clinical Neuropsychology. Springer, New York, USA, p.678-680.

[10]Devlin J, Chang MW, Lee K, et al., 2019. 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, p.4171-4186.

[11]Ekman P, 1999. Basic emotions. In: Dalgleish T, Dalgleish MJ (Eds.), Handbook of Cognition and Emotion. Wiley, New York, USA, p.301-320.

[12]Graves A, Schmidhuber J, 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neur Netw, 18(5-6):602-610.

[13]Hamm J, Kohler CG, Gur RC, et al., 2011. Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J Neurosci Meth, 200(2):237-256.

[14]Jaiswal S, Valstar MF, Gillott A, et al., 2017. Automatic detection of ADHD and ASD from expressive behaviour in RGBD data. Proc 12th IEEE Int Conf on Automatic Face & Gesture Recognition, p.762-769.

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

[16]Kuhn HW, 1955. The Hungarian method for the assignment problem. Nav Res Logist Q, 2(1-2):83-97.

[17]Leo M, Carcagn‘i P, Distante C, et al., 2018. Computational assessment of facial expression production in ASD children. Sensors, 18(11):3993.

[18]Lepetit V, Moreno-Noguer F, Fua P, 2009. EPnP: an accurate O(n) solution to the PnP problem. Int J Comput Vis, 81(2):155.

[19]Li J, Zhong YH, Han JX, et al., 2019. Classifying ASD children with LSTM based on raw videos. Neurocomputing, 390:226-238.

[20]Marcano JL, Bell MA, Beex AAL, 2018. Classification of ADHD and non-ADHD subjects using a universal background model. Biomed Signal Process Contr, 39:204-212.

[21]Martinez J, Hossain R, Romero J, et al., 2017. A simple yet effective baseline for 3d human pose estimation. Proc IEEE Int Conf on Computer Vision, p.2640-2649.

[22]Monchi O, Petrides M, Petre V, et al., 2001. Wisconsin card sorting revisited: distinct neural circuits participating in different stages of the task identified by event-related functional magnetic resonance imaging. J Neurosci, 21(19):7733-7741.

[23]Mu noz-Organero M, Powell L, Heller B, et al., 2019. Using recurrent neural networks to compare movement patterns in ADHD and normally developing children based on acceleration signals from the wrist and ankle. Sensors, 19(13):2935.

[24]Oerlemans AM, van der Meer JM, van Steijn DJ, et al., 2014. Recognition of facial emotion and affective prosody in children with ASD (+ADHD) and their unaffected siblings. Eur Child Adolesc Psych, 23(5):257-271.

[25]Polanczyk GV, Willcutt EG, Salum GA, et al., 2014. ADHD prevalence estimates across three decades: an updated systematic review and meta-regression analysis. Int J Epidemiol, 43(2):434-442.

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

[27]Sayal K, Prasad V, Daley D, et al., 2018. ADHD in children and young people: prevalence, care pathways, and service provision. Lancet Psych, 5(2):175-186.

[28]Saylor CF, Finch AJ, Spirito A, et al., 1984. The children’s depression inventory: a systematic evaluation of psychometric properties. J Consult Clin Psychol, 52(6):955-967.

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

[30]Thompson T, Lloyd A, Joseph A, et al., 2017. The Weiss functional impairment rating scale-parent form for assessing ADHD: evaluating diagnostic accuracy and determining optimal thresholds using ROC analysis. Qual Life Res, 26(7):1879-1885.

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

[32]Wang TT, Liu KH, Li ZZ, et al., 2017. Prevalence of attention deficit/hyperactivity disorder among children and adolescents in China: a systematic review and meta-analysis. BMC Psych, 17(1):32.

[33]Willcutt EG, Nigg JT, Pennington BF, et al., 2012. Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. J Abnorm Psychol, 121(4):991-1010.

[34]Zou L, Zheng JN, Miao CY, et al., 2017. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access, 5:23626-23636.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE