Deep learning for diagnosis of Alzheimer's disease in the past five years: a visualized analysis
10.3969/j.issn.1006-9771.2022.11.011
- VernacularTitle: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
- Author:
Jiarui JIANG
1
;
Zhendong NIU
1
Author Information
1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
- Publication Type:Journal Article
- Keywords:
deep learning;
Alzheimer's disease;
diagnosis;
visualized analysis
- From:
Chinese Journal of Rehabilitation Theory and Practice
2022;28(11):1318-1324
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveTo 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. MethodsThe 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. ResultsA 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. ConclusionDeep learning for the diagnosis of AD is mainly used for classification and early prediction of Alzheimer's disease.
