The application value of deep learning in imaging studies for predicting the conversion of Alzheimer's disease
10.3969/j.issn.1006-5725.2025.09.021
- VernacularTitle:深度学习在阿尔茨海默病疾病转化预测影像学研究中的应用价值
- Author:
Yingmei HAN
1
;
Yijie LI
;
Heng ZHANG
;
Weiqing LI
;
Ze FENG
;
Feng WANG
Author Information
1. 黑龙江中医药大学研究生院(黑龙江 哈尔滨 150040)
- Publication Type:Journal Article
- Keywords:
deep learning;
convolutional neural network;
Alzheimer's disease;
magnetic resonance imaging
- From:
The Journal of Practical Medicine
2025;41(9):1413-1424
- CountryChina
- Language:Chinese
-
Abstract:
Alzheimer's disease(AD),a neurodegenerative disorder,manifests pathological changes in the brain even during the asymptomatic stage.As the pathological burden intensifies,patients experience functional decline in multiple cognitive domains,including memory,language,spatial perception,executive function,and calculation,and may also exhibit emotional abnormalities.Once AD progresses,treatment becomes extremely chal-lenging.Therefore,early diagnosis and accurate prediction of disease conversion are core tasks in the prevention and treatment of AD,and they are also urgent scientific research challenges to be overcome.Deep learning(DL)models demonstrate considerable advantages in the diagnosis,prediction,classification,and feature extraction of AD,offering new hope for solving this challenging problem.This research commences with a concise introduction to the outcomes of AD and the fundamental knowledge of deep learning.Subsequently,it offers an overview of the imaging studies on the utilization of deep learning for predicting disease transformation from two perspectives.Firstly,it systematically summarizes the existing DL models that have demonstrated innovation in the classification and prediction performance of AD.Secondly,it provides a comprehensive outline of the DL fusion models applied to the diagnosis,classification,and prediction of AD.Finally,this paper expounds upon the impending challenges in the research of this domain.This article demonstrates that deep learning models is cutting-edge trends in the ex-ploration of AD research.
