深度学习在阿尔茨海默病诊断中应用近5年文献的可视化分析

doi:10.3969/j.issn.1006-9771.2022.11.011

摘要/Abstract

摘要：

目的对近5年在阿尔茨海默病(AD)诊断中应用深度学习的现状、研究热点和趋势进行可视化分析。

方法在Web of Science核心数据库检索2017年至2021年深度学习在AD诊断中应用的相关文献，采用CiteSpace 6.1.R3软件分别从年发文量、国家/地区、机构、作者、关键词、参考文献等方面进行可视化分析。

结果共检索到文献306篇。发文量逐年增加。美国、韩国、英国为高影响力国家，中国科学院为发文量最多和中心性最高的机构，Liu M为发文量最多的作者。研究热点为对AD各阶段的分类研究。使用独立且具有互补性的多模态数据进行AD各阶段分类和早期预测可能成为未来趋势。

结论深度学习主要用于对AD的分类和早期预测。

关键词: 深度学习, 阿尔茨海默病, 诊断, 可视化分析

Abstract:

Objective To analyze the current situation, research hotpots and trends of researches about the application of deep learning in the diagnosis of Alzheimer's disease (AD) in the past five years.

Methods The researches about the application of deep learning in the diagnosis of AD were retrieved in the core database of Web of Science, from 2017 to 2021, and analyzed with CiteSpace 6.1.R3 in terms of annual number of researches, countries/regions, institutions, authors, keywords and references.

Results A total of 306 researches were returned. The annual number increased year by year. United States, South Korea and United Kingdom were the highly influential countries, Chinese Academy of Sciences was the most frequently published and central institution, and Liu M was the author publishing the most researches. The researches mainly focused on the classification of various stages of AD. The classification of AD using independent and complementary multimodal data, and early prediction of AD might become a frontier trend.

Conclusion Deep learning for the diagnosis of AD is mainly used for classification and early prediction of Alzheimer's disease.

Key words: deep learning, Alzheimer's disease, diagnosis, visualized analysis

中图分类号:

R749.1

蒋嘉蕊,牛振东. 深度学习在阿尔茨海默病诊断中应用近5年文献的可视化分析[J]. 《中国康复理论与实践》, 2022, 28(11): 1318-1324.

JIANG Jiarui,NIU Zhendong. Deep learning for diagnosis of Alzheimer's disease in the past five years: a visualized analysis[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2022, 28(11): 1318-1324.

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参考文献 46

[1]	GOULDING M R. Public Health and Aging: Trends in Aging: United States and Worldwide (Morbidity and Mortality Weekly Report)[R]. Centers for Disease Control and Prevention, 2003.
[2]	KNOPMAN D S, AMIEVA H, PETERSEN R C, et al. Alzheimer disease[J]. Nat Rev Dis Primers, 2021, 7(1): 1-21. doi: 10.1038/s41572-020-00234-1
[3]	ALBERDI A, AZTIRIA A, BASARAB A. On the early diagnosis of Alzheimer's disease from multimodal signals: a survey[J]. Artif Intell Med, 2016, 71: 1-29. doi: 10.1016/j.artmed.2016.06.003 pmid: 27506128
[4]	YIANNOPOULOU K G, PAPAGEORGIOU S G. Current and future treatments in Alzheimer disease: an update[J]. J Cent Nerv Syst Dis, 2020, 12: 1179573520907397.
[5]	DUBOIS B, FELDMAN H H, JACOVA C, et al. Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria[J]. Lancet Neurol, 2007, 6(8): 734-746. doi: 10.1016/S1474-4422(07)70178-3 pmid: 17616482
[6]	楚阳, 徐文龙. 基于计算机辅助诊断技术的阿尔兹海默症早期分类研究综述[J]. 计算机工程与科学, 2022, 44(5): 879-893.
	CHU Y, XU W L. Review of early classification of Alzheimer's disease based on computer aided diagnosis technology[J]. Comput Eng Sci, 2022, 44(5): 879-893
[7]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
[8]	KRUTHIKA K R, MAHESHAPPA H D; Alzheimer's Disease Neuroimaging Initiative. CBIR system using Capsule Networks and 3D CNN for Alzheimer's disease diagnosis[J]. Inform Med Unlock, 2019, 14: 59-68. doi: 10.1016/j.imu.2018.12.001
[9]	王鑫, 邹海欧, 孙静. 基于CiteSpace的社区舒缓医疗国际研究现状与趋势分析[J]. 中国医学科学院学报, 2022, 44(3): 450-457.
	WANG X, ZOU H O, SUN J. Analysis of the status and trend of international research on community palliative care based on CiteSpace[J]. J Chin Acad Med Sci, 2022, 44(3): 450-457.
[10]	顾海, 奉子岚, 吴迪, 等. 我国远程医疗研究现状及趋势:基于CiteSpace的文献量化分析[J]. 信息资源管理学报, 2020, 10(4): 119-129.
	GU H, FENG Z L, WU D, et al. Current situation and trend of telemedicine research in China: quantitative analysis of literature based on CiteSpace[J]. J Inform Resource Manag, 2020, 10(4): 119-129.
[11]	侯剑华, 胡志刚. CiteSpace软件应用研究的回顾与展望[J]. 现代情报, 2013, 33(4): 99-103. doi: 10.3969/j.issn.1008-0821.2013.04.022
	HOU J H, HU Z G. Review and prospect of research on the application of SiteSpace software[J]. Modern Intell, 2013, 33(4): 99-103. doi: 10.3969/j.issn.1008-0821.2013.04.022
[12]	LIU M, LI F, YAN H, et al. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease[J]. Neuroimage, 2020, 208: 116459. doi: 10.1016/j.neuroimage.2019.116459
[13]	LIU M, CHENG D, WANG K, et al. Multi-modality cascaded convolutional neural networks for Alzheimer's disease diagnosis[J]. Neuroinformatics, 2018, 16(3): 295-308. doi: 10.1007/s12021-018-9370-4
[14]	AHMED M R, ZHANG Y, FENG Z, et al. Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects[J]. IEEE Rev Biomed Eng, 2018, 12: 19-33. doi: 10.1109/RBME.2018.2886237
[15]	WANG S H, ZHOU Q, YANG M, et al. ADVIAN: Alzheimer's disease VGG-inspired attention network based on convolutional block attention module and multiple way data augmentation[J]. Front Aging Neurosci, 2021, 13: 313.
[16]	PUNJABI A, MARTERSTECK A, WANG Y, et al. Neuroimaging modality fusion in Alzheimer's classification using convolutional neural networks[J]. PLoS One, 2019, 14(12): e0225759. doi: 10.1371/journal.pone.0225759
[17]	JIN D, ZHOU B, HAN Y, et al. Generalizable, reproducible, and neuroscientifically interpretable imaging biomarkers for Alzheimer's disease[J]. Adv Sci, 2020, 7(14): 2000675. doi: 10.1002/advs.202000675
[18]	SON H J, OH J S, OH M, et al. The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases[J]. Eur J Nuclear Med Mol Imaging, 2020, 47(2): 332-341. doi: 10.1007/s00259-019-04595-y
[19]	GHAZI M M, NIELSEN M, PAI A, et al. Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling[J]. Med Imag Analysis, 2019, 53: 39-46.
[20]	陈悦, 陈超美, 刘则渊, 等. CiteSpace知识图谱的方法论功能[J]. 科学学研究, 2015, 33(2): 242-253.
	CHEN Y, CHEN C M, LIU Z Y, et al. The methodology function of CiteSpace mapping knowledge domains[J]. Stud Sci Sci, 2015, 33(2): 242-253.
[21]	LIAN C, LIU M, ZHANG J, et al. Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI[J]. IEEE Trans Pattern Analysis Machine Intell, 2018, 42(4): 880-893. doi: 10.1109/TPAMI.2018.2889096
[22]	CHOI H, JIN K H. Alzheimer's Disease Neuroimaging Initiative. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging[J]. Behav Brain Res, 2018, 344: 103-109. doi: S0166-4328(18)30101-3 pmid: 29454006
[23]	ARBABSHIRANI M R, PLIS S, SUI J, et al. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls[J]. Neuroimage, 2017, 145: 137-165. doi: S1053-8119(16)00210-X pmid: 27012503
[24]	BI X, WANG H. Early Alzheimer's disease diagnosis based on EEG spectral images using deep learning[J]. Neural Networks, 2019, 114: 119-135. doi: S0893-6080(19)30048-6 pmid: 30903945
[25]	JACK C R, BERNSTEIN M A, FOX N C, et al. The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods[J]. J Magn Reson Imaging, 2008, 27(4): 685-691. doi: 10.1002/jmri.21049 pmid: 18302232
[26]	DONNELLY-KEHOE P A, PASCARIELLO G O, GÓMEZ J C, et al. Looking for Alzheimer's disease morphometric signatures using machine learning techniques[J]. J Neurosci Method, 2018, 302: 24-34. doi: 10.1016/j.jneumeth.2017.11.013
[27]	MARCUS D S, FOTENOS A F, CSERNANSKY J G, et al. Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults[J]. J Cogn Neurosci, 2010, 22(12): 2677-2684. doi: 10.1162/jocn.2009.21407
[28]	AREVALO-RODRIGUEZ I, SMAILAGIC N, ROQUÉ I FIGULS M, et al. Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI)[J]. Cochrane Database Syst Rev, 2015 (3): CD010783.
[29]	BI X, ZHAO X, HUANG H, et al. Functional brain network classification for Alzheimer's disease detection with deep features and extreme learning machine[J]. Cogn Comput, 2020, 12(3): 513-527. doi: 10.1007/s12559-019-09688-2
[30]	YU X, PENG B, SHI J, et al. 3D Convolutional networks based automatic diagnosis of Alzheimer's disease using structural MRI[C]. 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2020.
[31]	LEE E, CHOI J S, KIM M, et al. Toward an interpretable Alzheimer's disease diagnostic model with regional abnormality representation via deep learning[J]. Neuroimage, 2019, 202: 116113. doi: 10.1016/j.neuroimage.2019.116113
[32]	PARK C, HA J, PARK S. Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset[J]. Expert Syst Appl, 2020, 140: 112873. doi: 10.1016/j.eswa.2019.112873
[33]	JAIN R, JAIN N, AGGARWAL A, et al. Convolutional neural network based Alzheimer's disease classification from magnetic resonance brain images[J]. Cogn Syst Res, 2019, 57: 147-159. doi: 10.1016/j.cogsys.2018.12.015
[34]	TUFAIL A B, MA Y K, KAABAR M K A, et al. Classification of initial stages of Alzheimer's disease through PET neuroimaging modality and deep learning: quantifying the impact of image filtering approaches[J]. Mathematics, 2021, 9(23): 3101. doi: 10.3390/math9233101
[35]	FOLEGO G, WEILER M, CASSEB R F, et al. Alzheimer's disease detection through whole-brain 3D-CNN MRI[J]. Front Bioeng Biotechnol, 2020, 8: 534592. doi: 10.3389/fbioe.2020.534592
[36]	MARTINEZ-MURCIA F J, ORTIZ A, GORRIZ J M, et al. Studying the manifold structure of Alzheimer's disease: a deep learning approach using convolutional autoencoders[J]. IEEE J Biomed Health Inform, 2019, 24(1): 17-26. doi: 10.1109/JBHI.2019.2914970
[37]	GUO H, ZHANG Y. Resting state fMRI and improved deep learning algorithm for earlier detection of Alzheimer's disease[J]. IEEE Access, 2020, 8: 115383-115392. doi: 10.1109/ACCESS.2020.3003424
[38]	LIU Y, YAN Z. A combined deep-learning and Lattice Boltzmann model for segmentation of the hippocampus in MRI[J]. Sensors, 2020, 20(13): 3628. doi: 10.3390/s20133628
[39]	ZENG N, LI H, PENG Y. A new deep belief network-based multi-task learning for diagnosis of Alzheimer's disease[J]. Neural Comput Appl, 2021: 1-12.
[40]	LIU S, LIU S, CAI W, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease[J]. IEEE Trans Biomed Eng, 2014, 62(4): 1132-1140. doi: 10.1109/TBME.2014.2372011
[41]	BASAIA S, AGOSTA F, WAGNER L, et al. Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks[J]. NeuroImage, 2019, 21: 101645.
[42]	SUK H I, LEE S W, SHEN D, 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
[43]	ZHANG T, SHI M. Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer's disease[J]. J Neurosci Method, 2020, 341: 108795. doi: 10.1016/j.jneumeth.2020.108795
[44]	WEN J, THIBEAU-SUTRE E, DIAZ-MELO M, et al. Convolutional neural networks for classification of Alzheimer's disease: overview and reproducible evaluation[J]. Med Imag Analysis, 2020, 63: 101694.
[45]	PELKA O, FRIEDRICH C M, NENSA F, et al. Sociodemographic data and APOE-ε4 augmentation for MRI-based detection of amnestic mild cognitive impairment using deep learning systems[J]. PLoS One, 2020, 15(9): e0236868. doi: 10.1371/journal.pone.0236868
[46]	KANG L, JIANG J, HUANG J, et al. Identifying early mild cognitive impairment by multi-modality MRI-based deep learning[J]. Front Aging Neurosci, 2020, 12: 206. doi: 10.3389/fnagi.2020.00206 pmid: 33101003

分类号	大小	轮廓值	关键词
0	41	0.76	segmentation; early diagnosis; pattern; transfer learning; image classification
1	31	0.71	fusion; feature; selection; hippocampal; MRI; feature extraction
2	30	0.92	computer-aided diagnosis; classification; MCI; convolutional neural network; eeg
3	25	0.87	dementia; fMRI; network; recongnition; robust
4	22	0.96	ADNI; PET; sMRI; OASIS; big data
5	22	0.86	support vector machine; progression; deep neural network; machine learning
6	18	0.94	disease; feature ranking; image analysis; ensemble; bioelectronic medicine
7	14	0.93	amyloid beta; alpha synuclein; FDG PET; impairment; neurodegenerative disorder
8	14	0.94	voxel based morphometry; prediction; volumetry; anatomical landmark; patch
9	13	0.88	guideline; feature representation; Mini Mental State; 3d convolutional network; tau

频次	作者	期刊/会议	文题
43	Liu等	IEEE T Bio-Med Eng	Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease
42	Litjens等	Med Image Anal	A survey on deep learning in medical image analysis
36	Basaia等	Neuroimage-Clin	Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks
35	Hosseiniasl等	IEEE Image Proc	Alzheimer's disease diagnostics by adaptation of 3D convolutional network
35	Lecun等	Nature	Deep learning