The Hotspots and Frontier Trends of Artificial Intelligence in the Clinical Diagnosis and Treatment of Alzheimer's Disease: Bibliometric Analysis of the Past 20 Years

doi:10.12114/j.issn.1007-9572.2023.0704

Abstract

Abstract:

Background

Currently, the number of research papers on the application of artificial intelligence to the field of Alzheimer's disease (AD) has increased significantly. It is important to clarify the latest research hotspots and future development trends in this field.

Objective

To summarize the relevant research on the application of artificial intelligence to AD through bibliometric analysis, and clarify the research hotspots and trends from 2004 to 2023.

Methods

Literature on the application of artificial intelligence to AD from January 2004 to June 2023 was searched for in the Web of Science core database, and Microsoft Office Excel, CiteSpace, and VOSviewer software were used to visually analyze the number of publications, countries, authors, institutions, keywords, and co-citation networks of the literature.

Results

Ultimately 3 189 articles were included. The number of literature on the application of artificial intelligence to AD has steadily increased since 2004 and has grown rapidly since 2015, with a maximum of over 600 articles. A total of 94 countries, 3 930 institutions, 13 563 authors, and 52 019 cited authors participated in this study. Among them, the United States and China were in a leading position in this field; Republic of Korea universities ranked first in terms of the number of publications; In addition, ZHANG DAOQIANG, LIU MINGXIA, SUK HEUNG-IL, and CLIFFORD R. JACK Jr were not only prolific authors but also the authors with the most citations. The visualization analysis of keywords and literature citations revealed that regarding the application of artificial intelligence to AD, the diagnosis and disease course classification of AD, as well as the prediction of its risk factors, are current research hotspots and that task analysis are future research trends.

Conclusion

The application of artificial intelligence to AD has attracted widespread attention from researchers worldwide. The diagnosis and classification of AD, as well as the prediction of its risk factors, are current research hotspots. Developing adjunctive drugs in task analysis, personalized treatment and care, and improving the algorithm performance of artificial intelligence may be research trends in the future.

Key words: Alzheimer disease, Dementia, Artificial intelligence, CiteSpace, VOSviewer, Bibliometrics

摘要：

背景

目前，人工智能应用于阿尔茨海默病（AD）领域的研究论文数量增幅较大，明确该领域最新研究热点和未来发展趋势十分重要。

目的

通过应用文献计量学分析，总结人工智能应用于AD领域的相关研究，阐明2004—2023年的研究热点和未来研究趋势。

方法

在Web of Science核心数据库中检索了2004年1月—2023年6月关于人工智能应用于AD领域的文献。采用Microsoft Office Excel、CiteSpace和VOSviewer软件对发文量、国家、作者、机构、关键词和共引网络进行可视化分析。

结果

最终纳入了3 189篇文献。自2004年以来有关人工智能应用于AD领域的文献数量稳步增加，并从2015年起进入快速增长阶段，最高突破600篇。共有94个国家、3 930家机构、13 563位作者以及52 019位被引作者参与了此研究。其中，美国和中国在这一领域中处于领先地位；韩国大学发文量位列第一；此外，ZHANG DAOQIANG、LIU MINGXIA、SUK HEUNG-IL和CLIFFFORD R. JACK Jr不仅是高产的作者还是被引次数最多的作者。根据关键词和文献被引结果的可视化分析，发现AD的诊断与病程分类、预测AD的风险因素是当前的研究热点，任务分析是未来人工智能应用于AD领域中的研究趋势。

结论

人工智能应用于AD领域已经引起了全球研究者的广泛关注，AD的诊断与病程分类、预测AD的风险因素是当前的研究热点，而任务分析中的辅助药物的研发、个性化治疗和护理以及提升人工智能的算法性能可能会成为未来的研究趋势。

关键词: 阿尔茨海默病, 痴呆, 人工智能, CiteSpace, VOSviewer, 文献计量学

CLC Number:

R 745.7

YU Ruxia,JIANG Jing,WANG Qiucheng, et al. The Hotspots and Frontier Trends of Artificial Intelligence in the Clinical Diagnosis and Treatment of Alzheimer's Disease: Bibliometric Analysis of the Past 20 Years[J]. Chinese General Practice, 2024, 27(26): 3218-3226. DOI: 10.12114/j.issn.1007-9572.2023.0704.
余如霞,姜婧,王湫澄等. 人工智能在阿尔茨海默病临床诊疗中的研究热点及前沿趋势分析[J]. 中国全科医学, 2024, 27(26): 3218-3226. DOI: 10.12114/j.issn.1007-9572.2023.0704.

Figures/Tables 12

References 41

[1]	JACK C R Jr，BENNETT D A，BLENNOW K，et al. NIA-AA Research Framework：toward a biological definition of Alzheimer's disease[J]. Alzheimers Dement，2018，14（4）：535-562. DOI：10.1016/j.jalz.2018.02.018.
[2]	GUTIÉRREZ I L，DELLO RUSSO C，NOVELLINO F，et al. Noradrenaline in Alzheimer's disease：a new potential therapeutic target[J]. Int J Mol Sci，2022，23（11）：6143. DOI：10.3390/ijms23116143.
[3]	SCHELTENS P，DE STROOPER B，KIVIPELTO M，et al. Alzheimer's disease[J]. Lancet，2021，397（10284）：1577-1590. DOI：10.1016/S0140-6736(20)32205-4.
[4]	LEE G，NHO K，KANG B，et al. Predicting Alzheimer's disease progression using multi-modal deep learning approach[J]. Sci Rep，2019，9（1）：1952. DOI：10.1038/s41598-018-37769-z.
[5]	LI Z Y，JIANG X Q，WANG Y Z，et al. Applied machine learning in Alzheimer's disease research：omics，imaging，and clinical data[J]. Emerg Top Life Sci，2021，5（6）：765-777. DOI：10.1042/ETLS20210249.
[6]	GAO X R，CHIARIGLIONE M，QIN K，et al. Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer's disease prediction[J]. Sci Rep，2023，13（1）：450. DOI：10.1038/s41598-023-27551-1.
[7]	ALGHAMEDY F H，SHAFIQ M，LIU L J，et al. Machine learning-based multimodel computing for medical imaging for classification and detection of alzheimer disease[J]. Comput Intell Neurosci，2022，2022：9211477. DOI：10.1155/2022/9211477.
[8]	RAZA N，NASEER A，TAMOOR M，et al. Alzheimer disease classification through transfer learning approach[J]. Diagnostics，2023，13（4）：801. DOI：10.3390/diagnostics13040801.
[9]	MOYLE W，ARNAUTOVSKA U，OWNSWORTH T，et al. Potential of telepresence robots to enhance social connectedness in older adults with dementia：an integrative review of feasibility[J]. Int Psychogeriatr，2017，29（12）：1951-1964. DOI：10.1017/S1041610217001776.
[10]	SUK H I，LEE S W，SHEN D G，et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis[J]. NeuroImage，2014，101：569-582. DOI：10.1016/j.neuroimage.2014.06.077.
[11]	MORADI E，PEPE A，GASER C，et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. Neuroimage，2015，104：398-412. DOI：10.1016/j.neuroimage.2014.10.002.
[12]	LIU S Q，LIU S D，CAI W D，et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease[J]. IEEE Trans Biomed Eng，2015，62（4）：1132-1140. DOI：10.1109/TBME.2014.2372011.
[13]	KUMAR Y，KOUL A，SINGLA R，et al. Artificial intelligence in disease diagnosis：a systematic literature review，synthesizing framework and future research agenda[J]. J Ambient Intell Humaniz Comput，2023，14（7）：8459-8486. DOI：10.1007/s12652-021-03612-z.
[14]	MÁRQUEZ F，YASSA M A. Neuroimaging biomarkers for Alzheimer's disease[J]. Mol Neurodegener，2019，14（1）：21. DOI：10.1186/s13024-019-0325-5.
[15]	CARRILLO M C，ROWE C C，SZOEKE C，et al. Research and standardization in Alzheimer's trials：reaching international consensus[J]. Alzheimers Dement，2013，9（2）：160-168. DOI：10.1016/j.jalz.2012.10.006.
[16]	NGUYEN D T，RYU S，QURESHI M N I，et al. Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns[J]. PLoS One，2019，14（2）：e0212582. DOI：10.1371/journal.pone.0212582.
[17]	ETMINANI K，SOLIMAN A，DAVIDSSON A，et al. A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies，Alzheimer's disease，and mild cognitive impairment using brain 18F-FDG PET[J]. Eur J Nucl Med Mol Imaging，2022，49（2）：563-584. DOI：10.1007/s00259-021-05483-0.
[18]	ZHU W Y，SUN L，HUANG J S，et al. Dual attention multi-instance deep learning for Alzheimer's disease diagnosis with structural MRI[J]. IEEE Trans Med Imaging，2021，40（9）：2354-2366. DOI：10.1109/TMI.2021.3077079.
[19]	ZHANG D Q，WANG Y P，ZHOU L P，et al. Multimodal classification of Alzheimer's disease and mild cognitive impairment[J]. NeuroImage，2011，55（3）：856-867. DOI：10.1016/j.neuroimage.2011.01.008.
[20]	SALEEM T J，ZAHRA S R，WU F，et al. Deep learning-based diagnosis of Alzheimer's disease[J]. J Pers Med，2022，12（5）：815. DOI：10.3390/jpm12050815.
[21]	KIM J S，HAN J W，BAE J B，et al. Deep learning-based diagnosis of Alzheimer's disease using brain magnetic resonance images：an empirical study[J]. Sci Rep，2022，12（1）：18007. DOI：10.1038/s41598-022-22917-3.
[22]	CHEUNG C Y，RAN A R，WANG S J，et al. A deep learning model for detection of Alzheimer's disease based on retinal photographs：a retrospective，multicentre case-control study[J]. Lancet Digit Health，2022，4（11）：e806-815. DOI：10.1016/S2589-7500(22)00169-8.
[23]	KIVIMÄKI M，LIVINGSTON G，SINGH-MANOUX A，et al. Estimating dementia risk using multifactorial prediction models[J]. JAMA Netw Open，2023，6（6）：e2318132. DOI：10.1001/jamanetworkopen.2023.18132.
[24]	YOU J，ZHANG Y R，WANG H F，et al. Development of a novel dementia risk prediction model in the general population：a large，longitudinal，population-based machine-learning study[J]. EClinicalMedicine，2022，53：101665. DOI：10.1016/j.eclinm.2022.101665.
[25]	ZHOU X P，CHEN Y，IP F C F，et al. Deep learning-based polygenic risk analysis for Alzheimer's disease prediction[J]. Commun Med，2023，3（1）：49. DOI：10.1038/s43856-023-00269-x.
[26]	YAGI T，KANEKIYO M，ITO J，et al. Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer's disease in the Japanese Alzheimer's Disease Neuroimaging Initiative study[J]. Alzheimers Dement，2019，5：364-373. DOI：10.1016/j.trci.2019.06.004.
[27]	PETERSEN R C. Mild cognitive impairment as a diagnostic entity[J]. J Intern Med，2004，256（3）：183-194. DOI：10.1111/j.1365-2796.2004.01388.x.
[28]	LIN W M，GAO Q Q，YUAN J N，et al. Predicting Alzheimer's disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data[J]. Front Aging Neurosci，2020，12：77. DOI：10.3389/fnagi.2020.00077.
[29]	JO T，NHO K，SAYKIN A J. Deep learning in Alzheimer's disease：diagnostic classification and prognostic prediction using neuroimaging data[J]. Front Aging Neurosci，2019，11：220. DOI：10.3389/fnagi.2019.00220.
[30]	LUSTIG-BARZELAY Y，SHER I，SHARVIT-GINON I，et al. Machine learning for comprehensive prediction of high risk for Alzheimer's disease based on chromatic pupilloperimetry[J]. Sci Rep，2022，12（1）：9945. DOI：10.1038/s41598-022-13999-0.
[31]	SHIMODA A，LI Y，HAYASHI H，et al. Dementia risks identified by vocal features via telephone conversations：a novel machine learning prediction model[J]. PLoS One，2021，16（7）：e0253988. DOI：10.1371/journal.pone.0253988.
[32]	VATANSEVER S，SCHLESSINGER A，WACKER D，et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases：state-of-the-arts and future directions[J]. Med Res Rev，2021，41（3）：1427-1473. DOI：10.1002/med.21764.
[33]	TSUJI S，HASE T，YACHIE-KINOSHITA A，et al. Artificial intelligence-based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer's disease[J]. Alzheimers Res Ther，2021，13（1）：92. DOI：10.1186/s13195-021-00826-3.
[34]	DAS B，MATHEW A T，BAIDYA A T K，et al. Artificial intelligence assisted identification of potential tau aggregation inhibitors：ligand- and structure-based virtual screening，in silico ADME，and molecular dynamics study[J]. Mol Divers，2023. DOI：10.1007/s11030-023-10645-3.
[35]	FANG J S，ZHANG P Y，WANG Q，et al. Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease[J]. Alzheimers Res Ther，2022，14（1）：7. DOI：10.1186/s13195-021-00951-z.
[36]	SHU S，WOO B K. Use of technology and social media in dementia care：current and future directions[J]. World J Psychiatry，2021，11（4）：109-123. DOI：10.5498/wjp.v11.i4.109.
[37]	THYRIAN J R，HERTEL J，WUCHERER D，et al. Effectiveness and safety of dementia care management in primary care：a randomized clinical trial[J]. JAMA Psychiatry，2017，74（10）：996-1004. DOI：10.1001/jamapsychiatry.2017.2124.
[38]	MOON S，PARK K. The effect of digital reminiscence therapy on people with dementia：a pilot randomized controlled trial[J]. BMC Geriatr，2020，20（1）：166. DOI：10.1186/s12877-020-01563-2.
[39]	CAI X L，XIE D J，MADSEN K H，et al. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data[J]. Hum Brain Mapp，2020，41（1）：172-184. DOI：10.1002/hbm.24797.
[40]	LI Q，YANG M Q. Comparison of machine learning approaches for enhancing Alzheimer's disease classification[J]. PeerJ，2021，9：e10549. DOI：10.7717/peerj.10549.
[41]	AN N，DING H T，YANG J Y，et al. Deep ensemble learning for Alzheimer's disease classification[J]. J Biomed Inform，2020，105：103411. DOI：10.1016/j.jbi.2020.103411.

步骤	检索词
1	dementia；Alzheimer Disease；Alzheimer's Disease
2	artificial intelligence；machine intelligence；computational intelligence；intelligent learning；machine learning；deep learning；neural learning；feature^* learning；supervised learning；neural network^；unsupervised clustering；feature^ mining；data mining；deep network^；image^ segmentation；graph mining；feature^* selection；data clustering；semantic segmentation；knowledge graph；feature^* extraction；big data；expert^* system^；bayes^ network；neural nets model
3	1 AND 2

步骤	检索词
1	dementia；Alzheimer Disease；Alzheimer's Disease
2	artificial intelligence；machine intelligence；computational intelligence；intelligent learning；machine learning；deep learning；neural learning；feature^* learning；supervised learning；neural network^；unsupervised clustering；feature^ mining；data mining；deep network^；image^ segmentation；graph mining；feature^* selection；data clustering；semantic segmentation；knowledge graph；feature^* extraction；big data；expert^* system^；bayes^ network；neural nets model
3	1 AND 2

顺位	共被引作者	被引频次（次）
1	CLIFFFORD R. JACK Jr	1 388
2	PETERSEN RONALD C	808
3	ZHANG DAOQIANG	600
4	SUK HEUNG-IL	535
5	FISCHL BRUCE	510
6	ASHBURNER JOHN	484
7	BRAAK HEIKO	409
8	LIU MANHUA	406
9	LIU MINGXIA	403
10	DUBOIS BRUNO	398

顺位	共被引作者	被引频次（次）
1	CLIFFFORD R. JACK Jr	1 388
2	PETERSEN RONALD C	808
3	ZHANG DAOQIANG	600
4	SUK HEUNG-IL	535
5	FISCHL BRUCE	510
6	ASHBURNER JOHN	484
7	BRAAK HEIKO	409
8	LIU MANHUA	406
9	LIU MINGXIA	403
10	DUBOIS BRUNO	398

类别	名称	频次（次）
模型	machine learning（机器学习）	460
	deep learning（深度学习）	322
	convolutional neural network（卷积神经网络）	146
	neural network（神经网络）	118
	support vector machine（支持向量机）	104
功能	classification（分类）	577
	diagnosis（诊断）	476
	prediction（预测）	292
	feature selection（特征提取）	201
	segmentation（分割）	171