近20年国际人工智能赋能特殊儿童诊断及干预研究的可视化分析

doi:10.3969/j.issn.1006-9771.2024.04.005

Abstract

Abstract:

Objective To analyze the current status, hot topics and evolution paths of international research on artificial intelligence (AI) enabled clinical diagnosis and intervention for special children in the past 20 years, and to predict the future research trends.

Methods The relevant literature of AI enabled diagnosis and intervention for special children in the Web of Science Core Collection (WoSCC) database from 2004 to 2023 was retrieved. CiteSpace 6.3.R1 was used to visualize the publication volume, countries (regions), institutions, authors, co-occurrence keywords, keyword clustering and time zone map of international researches.

Results A total of 314 articles were included. Famous universities in Europe and America dominated the research on AI enabled diagnosis and intervention for special children. Journal of Autism and Developmental Disorders was the journal with the most publications. The United States was the country with the most publications. The research team with the most publications was led by Acharya U Rajendra, whose main contribution was to establish data testing and diagnosis models for autism spectrum disorder (ASD) patients. The main keywords were artificial intelligence, ASD, children, machine learning, deep learning, classification, diagnosis, adolescents, individual, recognition, etc. The LLR keyword clustering analysis resulted in ten clusters. The research hotspots were diagnosis, ASD, attention deficit hyperactivity disorder, deep learning and machine learning. The research subjects mainly included ASD, attention deficit hyperactivity disorder, learning disability, Down syndrome, visual impairment, cerebral palsy and intellectual disability. AI technologies mainly included deep neural network, smart watch, facial expression recognition, virtual reality, robot, machine learning, assistive technology, smart glasses, etc. The application scenarios mainly included diagnosis and screening, treatment, social communication, attention training, navigation and object recognition, motion analysis and treatment, and cognitive therapy, etc. The evolution path and development trend of this field were: the diagnosis and intervention services for special children came from the initial cerebral palsy and learning disability, to multiple types; from the initial classification and diagnosis, to multiple aspects of recognition and intervention; from the initial traditional technology, to new technologies, providing more technical support and method innovation for their personalized and precise education, and forming a multi-technology combination intervention mode.

Conclusion The application of AI technologies in the diagnosis and intervention of children with disabilities shows a significant upward trend. The focus of research is gradually shifting from basic classification and diagnosis to more complex identification and personalized intervention, particularly in areas such as ASD and attention deficit hyperactivity disorder. The use of cutting-edge technologies like deep learning and machine learning is propelling the field towards more precise and personalized treatment. The intervention model combining multiple technologies is becoming the future trend of development in this field.

Key words: artificial intelligence, children with disabilities, diagnosis, intervention, visualized analysis

CLC Number:

WANG Zhenzhou, ZHANG Yang. International researches on artificial intelligence enabled diagnosis and intervention for children with disabilities in the past two decades: a visualized analysis[J]. Chinese Journal of Rehabilitation Theory and Practice, 2024, 30(4): 404-415.

Figures/Tables 11

Figure1

Table1

Figure 2

Table 2

Table 3

Co-occurrence authors cluster analysis"

聚类ID	包含作者	共现关键词	高被引研究内容
1	Abdar Moloud、Acharya U Rajendra、Akhondzadeh Mohammadsadegh、Barua Prabal Datta	人工智能技术、卷积神经网络、孤独症谱系障碍、脑图谱、脑成像数据集、功能连接模式等	使用卷积神经网络自动检测ASD；基于功能连接模式对ABIDE多中心数据集的fMRI数据进行分类；可用于更多数据测试和ASD患者预筛选的模型^[5-6]
2	Rahman Muhammad Arifur、Burton Andrew、Kaiser M Shamim、Shopland Nicholas、Mahmud Mufti、Bronw David J	人工智能、孤独症谱系障碍、情绪检测、传感器数据、学习方法、游戏和任务、警报等	设计并实现一个基于AI的系统，用于监测和调整ASD患者的情况和学习方式；使用传感器数据捕捉患者的情绪和面部表情，并根据这些信息选择合适的游戏和任务进行干预；开发一个警报机制，当患者的行为出现异常时，及时通知照顾者和父母^[7⇓-9]
3	Afif Mouna、Ayachi Riadh、Said Yahia、Atri Mohamed	人工智能、室内物体检测、视觉障碍人士、深度卷积神经网络、RetinaNet等	探讨室内物体检测对视觉障碍人士导航辅助的重要性；基于深度卷积神经网络框架构建一个新的室内物体检测器；使用不同的网络结构作为RetinaNet的主干网络进行评估^[10⇓-12]
4	Aydas Buket、Bahado-singh Ray O、Radhakrishna Uppala、Vishweswaraiah Sangeetha	人工智能、DNA甲基化、脑性瘫痪、表观遗传学、神经退行性疾病、新生儿等	用DNA甲基化分析和AI预测新生儿脑性瘫痪；找到影响神经功能的甲基化位点和基因；建立高精度的预测模型；揭示脑性瘫痪的发病机制^[13⇓-15]
5	Wall Dennis P、Haber Nick、Kline Aaron、 Kalantarian Haik、Tariq Qandeel、Washington Peter、Daniels Jena	人工智能、软件医疗器械、孤独症谱系障碍、诊断、机器学习、梯度提升决策树、情绪识别、逻辑回归分类器等	测试一个基于AI的软件医疗器械，用于辅助初级保健医生诊断ASD；探讨使用Google Glass作为儿童ASD治疗工具的可行性，包括佩戴舒适度、情绪分类效果和情绪识别差异^[16⇓-18]
6	Meng Ying-Ru、Wu Tung-Kuang、Huang Shian-Chang	人工智能、人工神经网络、支持向量机、学习障碍、识别、诊断等	探讨使用人工神经网络和支持向量机这两种AI技术来识别和诊断学习障碍的学生的可行性和有效性；基于学术研究的经验性发现，提出一种基于计算机的AI方法^[19⇓-21]

Table 3

Table 4

Figure 3

Table 5

Figure 4

Table 6

Figure 5

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序号	期刊名称	载文量	年份	中心性
1	J Autism Dev Disord	122	2007	0.13
2	PLoS One	121	2015	0.04
3	Lect Notes Comput Sci	84	2008	0.05
4	IEEE Access	75	2020	0.02
5	Autism Res	62	2012	0.08
6	Sensors Basel	61	2020	0.02
7	Neuroimage Clin	60	2018	0.03
8	Sci Rep UK	60	2020	0.01
9	Arxiv	60	2017	0.03
10	Proc CVPR IEEE	55	2019	0.01

序号	姓名	所在机构	发文量/n
1	Acharya U Rajendra	Kumamoto University, Research Organization for Advanced Science and Technology (IROAST)	8
2	Mahumd Mufti	Nottingham Trent University, Department of Computer Science	7
3	Wall Dennis P	Harvard University, School of Medicine, Department of Biomedical Informatics	5
4	Bronw David J	Nottingham Trent University, Computer Science Team, Interactive Systems for Social Inclusion	5
5	Atri Mohamed	Massachusetts General Hospital Psychiatry Academy	5
6	Afif Mouna	University of Monastir, Faculty of Sciences of Monastir, Laboratory of Electronics and Microelectronics (EME)	5
7	Thabtah Fadi	Manukau Institute of Technology Department of Digital Technologies	5
8	Barua Prabal Datta	Singapore University of Social Sciences, School of Science and Technology	4
9	Burton Andrew	Nottingham Trent Univ, Interact Syst Res Grp, Comp Sci Team	4
10	Shopland Nicholas	Nottingham Trent University, Interactive Systems Research Group, Computer Science Team	4
11	Washington Peter	Stanford University, Department of Biomedical Data Science	4
12	Tariq Qandeel	Manukau Institute of Technology Department of Digital Technologies	4
13	Rahman Muhammad Arifur	Nottingham Trent University, Interactive Systems Research Group, Computer Science and Informatics	4
14	Aydas Buket	Albion College, Department of Mathematics and Computer Science	4
15	Bahado-sing Ray O	Oakland University, William Beaumont School of Medicine, Department of Obstetrics and Gynecology	4
16	Wu Tung-Kuang	Changhua University of Education, Department of Information Management	4
17	Meng Ying-Ru	Hsinchu University of Education, Department of Special Education	4
18	Huang Shian-Chang	Changhua University of Education, Department of Business Administration	4

序号	机构	中心性	年份	发文量/n	占比
1	Harvard University	0.03	2012	8	2.55%
2	Stanford University	0.00	2019	7	2.23%
3	Egyptian Knowledge Bank	0.08	2012	7	2.23%
4	Singapore University of Social Sciences	0.00	2016	6	1.91%
5	Centre National de la Recherche Scientific	0.10	2012	6	1.91%
6	Harvard Medical School	0.00	2012	5	1.59%
7	University of Technology Sydney	0.09	2020	5	1.59%
8	King Khalid University	0.00	2015	5	1.59%
9	University System of Ohio	0.00	2012	5	1.59%

研究对象	支撑技术	应用场景
ASD	深度神经网络、智能手表、ABIDE数据集、面部表情识别	诊断与筛查、治疗、社会沟通
ADHD	心电图、虚拟现实、机器人	诊断与筛查、注意力训练、监测
学习障碍	机器学习、增强与替代性交流、虚拟现实、辅助技术	特殊教育、融合教育(理解能力、听说读写)
Down综合征	支持向量机、特征选择	诊断与筛查和神经心理学研究
视力障碍	辅助技术、智能眼镜、物体检测、虚拟现实	导航和物体识别
脑瘫	选择、键盘仿真器、康复机器人、运动训练系统等	运动分析与治疗
智力障碍	社交机器人、虚拟现实、认知游戏系统	认知治疗、注意力训练、自我管理

International researches on artificial intelligence enabled diagnosis and intervention for children with disabilities in the past two decades: a visualized analysis

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序号	关键词	频次	年份	中介中心性
1	artificial intelligence	110	2004	0.33
2	children	88	2012	0.26
3	autism spectrum disorder	80	2011	0.19
4	machine learning	71	2007	0.08
5	deep learning	55	2018	0.06
6	classification	46	2006	0.20
7	diagnosis	34	2006	0.15
8	adolescents	19	2016	0.02
9	individuals	17	2021	0.02
10	identification	16	2012	0.04

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