The Journal of Practical Medicine >
The application value of deep learning in imaging studies for predicting the conversion of Alzheimer′s disease
Received date: 2024-12-27
Online published: 2025-05-20
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 challenging. 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 exploration of AD research.
Yingmei HAN , Yijie LI , Heng ZHANG , Weiqing LI , Ze FENG , Feng WANG . The application value of deep learning in imaging studies for predicting the conversion of Alzheimer′s disease[J]. The Journal of Practical Medicine, 2025 , 41(9) : 1413 -1424 . DOI: 10.3969/j.issn.1006-5725.2025.09.021
| 1 | CHU C S， WANG D Y， LIANG C K， et al. Automated Video Analysis of Audio-Visual Approaches to Predict and Detect Mild Cognitive Impairment and Dementia in Older Adults［J］. J Alzheimers Dis， 2023， 92（3）： 875-886. doi:10.3233/jad-220999 |
| 2 | YIN C， IMMS P， CHENG M， et al. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment［J］. Proc Natl Acad Sci U S A， 2023， 120（2）： e2214634120. |
| 3 | KAM T E， ZHANG H， JIAO Z， et al. Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection［J］. IEEE Trans Med Imaging， 2020， 39（2）： 478-487. doi:10.1109/tmi.2019.2928790 |
| 4 | MORA-RUBIO A， BRAVO-ORTIZ M A， QUINONES ARREDONDO S， et al. Classification of Alzheimer's disease stages from magnetic resonance images using deep learning［J］. PeerJ Comput Sci， 2023， 9： e1490. doi:10.7717/peerj-cs.1490 |
| 5 | 郭润财， 王蕾， 黄振国， 等. 基于从头训练模式深度学习卷积神经网络模型评估急性肺栓塞的价值［J］. 实用医学杂志， 2023， 39（22）： 2979-2983. doi:10.3969/j.issn.1006-5725.2023.22.021 |
| 6 | 李欣雨， 吴洋， 张红梅， 等. 深度学习技术在超声心动图图像质量控制中的应用［J］. 实用医学杂志， 2024， 40（1）： 108-113. doi:10.3969/j.issn.1006-5725.2024.01.019 |
| 7 | 谭立玮， 张淑军， 韩琪， 等. 面向医学影像报告生成的门归一化编解码网络［J］. 智能系统学报， 2024， 19（2）： 411-419. doi:10.11992/tis.202207013 |
| 8 | 韩英妹， 李一杰， 张衡， 等. 基于MRI分析阿尔茨海默病大尺度脑网络研究进展［J］. 实用医学杂志， 2024， 40（4）： 575-579. |
| 9 | 曾安， 贾龙飞， 潘丹， 等. 基于卷积神经网络和集成学习的阿尔茨海默症早期诊断［J］. 生物医学工程学杂志， 2019， 36（5）： 711-719. doi:10.7507/1001-5515.201809040 |
| 10 | OLAIMAT M AL， MARTINEZ J， SAEED F， et al. PPAD： a deep learning architecture to predict progression of Alzheimer's disease［J］. Bioinformatics， 2023， 39（39 ）： i149-i157. doi:10.1093/bioinformatics/btad249 |
| 11 | GKENIOS G， LATSIOU K， DIAMANTARAS K， et al. Diagnosis of Alzheimer's disease and Mild Cognitive Impairment using EEG and Recurrent Neural Networks［J］. Annu Int Conf IEEE Eng Med Biol Soc， 2022， 2022： 3179-3182. doi:10.1109/embc48229.2022.9871302 |
| 12 | ALESSANDRINI M， BIAGETTI G， CRIPPA P， et al. EEG-Based Alzheimer's Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network［J］. Sensors （Basel）， 2022， 22（10）：3696. doi:10.3390/s22103696 |
| 13 | NARASIMHAN R， GOPALAN M， SIKKANDAR M Y， et al. Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers［J］. Sensors （Basel）， 2023， 23（21）：8867. doi:10.3390/s23218867 |
| 14 | AQEEL A， HASSAN A， KHAN M A， et al. A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease［J］. Sensors （Basel）， 2022， 22（4）：1475. doi:10.3390/s22041475 |
| 15 | FARHATULLAH， CHEN X， ZENG D， et al. A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data［J］. Neural Netw， 2024， 181： 106778. doi:10.1016/j.neunet.2024.106778 |
| 16 | QIU S， JOSHI P S， MILLER M I， et al. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification［J］. Brain， 2020， 143（6）： 1920-1933. doi:10.1093/brain/awaa137 |
| 17 | AHMED G， ER M J， FAREED M M S， et al. DAD-Net： Classification of Alzheimer's Disease Using ADASYN Oversampling Technique and Optimized Neural Network［J］. Molecules， 2022， 27（20）：7085. doi:10.3390/molecules27207085 |
| 18 | WANG B， LIM J S. Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments［J］. Sensors （Basel）， 2022， 22（22）：8887. doi:10.3390/s22228887 |
| 19 | YAN H， MUBONANYIKUZO V， KOMOLAFE T E， et al. Hybrid-RViT： Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer's disease detection［J］. PLoS One， 2025， 20（2）： e0318998. doi:10.1371/journal.pone.0318998 |
| 20 | ALP S， AKAN T， BHUIYAN M S， et al. Joint transformer architecture in brain 3D MRI classification： its application in Alzheimer's disease classification［J］. Sci Rep， 2024， 14（1）： 8996. doi:10.1038/s41598-024-59578-3 |
| 21 | QU C， ZOU Y， MA Y， et al. Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer's Disease： A Systematic Review and Meta-Analysis［J］. Front Aging Neurosci， 2022， 14： 841696. doi:10.3389/fnagi.2022.841696 |
| 22 | QIU S， MILLER M I， JOSHI P S， et al. Multimodal deep learning for Alzheimer's disease dementia assessment［J］. Nat Commun， 2022， 13（1）： 3404. doi:10.1038/s41467-022-31037-5 |
| 23 | LOGAN R， WILLIAMS B G， FERREIRA DA SILVA M， et al. Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer's Disease Image Data Classification［J］. Front Aging Neurosci， 2021， 13： 720226. doi:10.3389/fnagi.2021.720226 |
| 24 | CHEN X， TANG M， LIU A， et al. Diagnostic accuracy study of automated stratification of Alzheimer's disease and mild cognitive impairment via deep learning based on MRI［J］. Ann Transl Med， 2022， 10（14）： 765. doi:10.21037/atm-22-2961 |
| 25 | 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 |
| 26 | JIANG J， KANG L， HUANG J， et al. Deep learning based mild cognitive impairment diagnosis using structure MR images［J］. Neurosci Lett， 2020， 730： 134971. doi:10.1016/j.neulet.2020.134971 |
| 27 | EL-ASSY A M， AMER H M， IBRAHIM H M， et al. A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data［J］. Sci Rep， 2024， 14（1）： 3463. doi:10.1038/s41598-024-53733-6 |
| 28 | SO J H， MADUSANKA N， CHOI H K， et al. Deep Learning for Alzheimer's Disease Classification using Texture Features［J］. Curr Med Imaging Rev， 2019， 15（7）： 689-698. doi:10.2174/1573405615666190404163233 |
| 29 | BALAJI P， CHAURASIA M A， BILFAQIH S M， et al. Hybridized Deep Learning Approach for Detecting Alzheimer's Disease［J］. Biomedicines， 2023， 11（1）：149. doi:10.3390/biomedicines11010149 |
| 30 | DENG L， WANG Y， Alzheimer's Disease Neuroimaging I. Fully Connected Multi-Kernel Convolutional Neural Network Based on Alzheimer's Disease Diagnosis［J］. J Alzheimers Dis， 2023， 92（1）： 209-228. doi:10.3233/jad-220519 |
| 31 | GHAFFARI H， TAVAKOLI H， PIRZAD JAHROMI G. Deep transfer learning-based fully automated detection and classification of Alzheimer's disease on brain MRI［J］. Br J Radiol， 2022， 95（1136）： 20211253. doi:10.1259/bjr.20211253 |
| 32 | BALBONI E， NOCETTI L， CARBONE C， et al. The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects［J］. Hum Brain Mapp， 2022， 43（11）： 3427-3438. doi:10.1002/hbm.25858 |
| 33 | BAE J， STOCKS J， HEYWOOD A， et al. Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network［J］. Neurobiol Aging， 2021， 99： 53-64. doi:10.1016/j.neurobiolaging.2020.12.005 |
| 34 | BAGHDADI N A， MALKI A， BALAHA H M， et al. A（3）C-TL-GTO： Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer［J］. Sensors （Basel）， 2022， 22（11）：4250. doi:10.3390/s22114250 |
| 35 | JIN L， ZHAO K， ZHAO Y， et al. A Hybrid Deep Learning Method for Early and Late Mild Cognitive Impairment Diagnosis With Incomplete Multimodal Data［J］. Front Neuroinform， 2022， 16： 843566. doi:10.3389/fninf.2022.843566 |
| 36 | ALI I， SALEEM N， ALHUSSEIN M， et al. DeepCGAN： early Alzheimer's detection with deep convolutional generative adversarial networks［J］. Front Med （Lausanne）， 2024， 11： 1443151. doi:10.3389/fmed.2024.1443151 |
| 37 | SAJJAD M， RAMZAN F， KHAN M U G， et al. Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography （PET） and synthetic data augmentation［J］. Microsc Res Tech， 2021， 84（12）： 3023-3034. doi:10.1002/jemt.23861 |
| 38 | KANG W， LIN L， SUN S， et al. Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer's disease staging［J］. Sci Rep， 2023， 13（1）： 5750. doi:10.1038/s41598-023-33055-9 |
| 39 | ZUO Q， LU L， WANG L， et al. Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis［J］. Front Neurosci， 2022， 16： 1087176. doi:10.3389/fnins.2022.1087176 |
| 40 | BI X A， WANG Y， LUO S， et al. Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data［J］. IEEE Trans Neural Netw Learn Syst， 2024， 35（6）： 7420-7434. doi:10.1109/tnnls.2022.3212700 |
| 41 | SINHA S， THOMOPOULOS S I， LAM P， et al. Alzheimer's Disease Classification Accuracy is Improved by MRI Harmonization based on Attention-Guided Generative Adversarial Networks［J］. Proc SPIE Int Soc Opt Eng， 2021， 12088. doi:10.1117/12.2606155 |
| 42 | SHI R， SHENG C， JIN S， et al. Generative adversarial network constrained multiple loss autoencoder： A deep learning-based individual atrophy detection for Alzheimer's disease and mild cognitive impairment［J］. Hum Brain Mapp， 2023， 44（3）： 1129-1146. doi:10.1002/hbm.26146 |
| 43 | PAN Y， LIU M， LIAN C， et al. Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer's Disease Diagnosis［J］. Med Image Comput Comput Assist Interv， 2018， 11072： 455-463. doi:10.1007/978-3-030-00931-1_52 |
| 44 | MA D， LU D， POPURI K， et al. Differential Diagnosis of Frontotemporal Dementia， Alzheimer's Disease， and Normal Aging Using a Multi-Scale Multi-Type Feature Generative Adversarial Deep Neural Network on Structural Magnetic Resonance Images［J］. Front Neurosci， 2020， 14： 853. doi:10.3389/fnins.2020.00853 |
| 45 | HOANG G M， KIM U H， KIM J G. Vision transformers for the prediction of mild cognitive impairment to Alzheimer's disease progression using mid-sagittal sMRI［J］. Front Aging Neurosci， 2023， 15： 1102869. doi:10.3389/fnagi.2023.1102869 |
| 46 | KHATRI U， KWON G R. Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer's Disease Progression Using 18F-FDG PET［J］. Bioengineering （Basel）， 2023， 10（10）. doi:10.3390/bioengineering10101225 |
| 47 | HUANG F， QIU A. Ensemble Vision Transformer for Dementia Diagnosis［J］. IEEE J Biomed Health Inform， 2024， 28（9）： 5551-5561. doi:10.1109/jbhi.2024.3412812 |
| 48 | SHAFFI N， VISWAN V， MAHMUD M. Ensemble of vision transformer architectures for efficient Alzheimer's Disease classification［J］. Brain Inform， 2024， 11（1）： 25. doi:10.1186/s40708-024-00238-7 |
| 49 | TANG C， WEI M， SUN J， et al. CsAGP： Detecting Alzheimer's disease from multimodal images via dual-transformer with cross-attention and graph pooling［J］. J King Saud Univ Comput Inf Sci， 2023， 35（7）： 101618. doi:10.1016/j.jksuci.2023.101618 |
| 50 | SAOUD L S， ALMARZOUQI H. Explainable early detection of Alzheimer's disease using ROIs and an ensemble of 138 3D vision transformers［J］. Sci Rep， 2024， 14（1）： 27756. doi:10.1038/s41598-024-76313-0 |
| 51 | ZHAO Z， YEOH P S Q， ZUO X， et al. Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans［J］. Front Neurol， 2024， 15： 1490829. doi:10.3389/fneur.2024.1490829 |
| 52 | HONG X， HUANG K， LIN J， et al. Combined Multi-Atlas and Multi-Layer Perception for Alzheimer's Disease Classification［J］. Front Aging Neurosci， 2022， 14： 891433. doi:10.3389/fnagi.2022.891433 |
| 53 | ALMUBARK I， CHANG L C， SHATTUCK K F， et al. A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease［J］. Front Aging Neurosci， 2020， 12： 603179. doi:10.3389/fnagi.2020.603179 |
| 54 | MUKHERJI D， MUKHERJI M， MUKHERJI N， et al. Early detection of Alzheimer's disease using neuropsychological tests： a predict-diagnose approach using neural networks［J］. Brain Inform， 2022， 9（1）： 23. doi:10.1186/s40708-022-00169-1 |
| 55 | CHEN K， WENG Y， HOSSEINI A A， et al. A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer's Disease involving data synthesis［J］. Neural Netw， 2024， 169： 442-452. doi:10.1016/j.neunet.2023.10.040 |
| 56 | LI M， JIANG Y， LI X， et al. Ensemble of convolutional neural networks and multilayer perceptron for the diagnosis of mild cognitive impairment and Alzheimer's disease［J］. Med Phys， 2023， 50（1）： 209-225. doi:10.1002/mp.15985 |
| 57 | QIANG Y R， ZHANG S W， LI J N， et al. Diagnosis of Alzheimer's disease by joining dual attention CNN and MLP based on structural MRIs， clinical and genetic data［J］. Artif Intell Med， 2023， 145： 102678. doi:10.1016/j.artmed.2023.102678 |
| 58 | FARHATULLAH， CHEN X， ZENG D， et al. 3-Way hybrid analysis using clinical and magnetic resonance imaging for early diagnosis of Alzheimer's disease［J］. Brain Res， 2024， 1840： 149021. doi:10.1016/j.brainres.2024.149021 |
| 59 | YAO M， LIU J， PU Y， et al. Multi-class Prediction of Cognitively Normal / Mild Cognitive Impairment / Alzheimer's Disease Status in Dementia Based on Convolutional Neural Networks with Attention Mechanism［J］. Annu Int Conf IEEE Eng Med Biol Soc， 2024， 2024： 1-7. doi:10.1109/embc53108.2024.10781557 |
| 60 | 颜彬. 基于注意力机制与全卷积网络的阿尔茨海默病分类方法研究［D］. 杭州：浙江理工大学，2023. |
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