A Review of Multimodal Fusion–Based Deep Learning for Alzheimer’s Disease

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works

Long Short-Term Memory

Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2_4 · Cited by 2,727 · see citing works

Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies.

Zhang, J., Zhang, Y., Wang, J., Xia, Y., Zhang, J., & Chen, L. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01911-3 · Cited by 1,138 · see citing works

Deep Learning Approach for Early Detection of Alzheimer’s Disease

Helaly, H. A., Badawy, M., & Haikal, A. Y. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09946-2 · Cited by 397 · see citing works

Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network

Huang, Y., Xu, J., Zhou, Y., Tong, T., Zhuang, X., & (ADNI), T. A. D. N. I. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00509 · Cited by 303 · see citing works

Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM

Feng, C., Elazab, A., Yang, P., Wang, T., Zhou, F., Hu, H., Xiao, X., & Lei, B. (2019) · IEEE Access · doi:10.1109/access.2019.2913847 · Cited by 289 · see citing works

Multi-Modal Graph Learning for Disease Prediction

Zheng, S., Zhu, Z., Liu, Z., Guo, Z., Liu, Y., Yang, Y., & Zhao, Y. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3159264 · Cited by 196 · see citing works

Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer's Disease

Gao, X., Shi, F., Shen, D. G., & Liu, M. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3097721 · Cited by 188 · see citing works

An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis

Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., & Shen, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.637386 · Cited by 178 · see citing works

Plasma Aβ42/Aβ40 and phospho‐tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease

Rissman, R. A., Langford, O., Raman, R., Donohue, M. C., Abdel‐Latif, S., Meyer, M. R., Wente‐Roth, T., Kirmess, K. M., Ngolab, J., Winston, C. N., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.13542 · Cited by 157 · see citing works

Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer’s Disease

Lin, W., Lin, W.-M., Chen, G., Zhang, H., Gao, Q., Huang, Y., Tong, T., Du, M., & Initiative, T. A. D. N. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.646013 · Cited by 127 · see citing works

Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET

Castellano, G., Esposito, A., Lella, E., Montanaro, G., & Vessio, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-56001-9 · Cited by 124 · see citing works

Multi-modal graph neural network for early diagnosis of Alzheimer's disease from sMRI and PET scans

Zhang, Y., He, X., Chan, Y. H., Teng, Q., & Rajapakse, J. C. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107328 · Cited by 117 · see citing works

Multi-modal cross-attention network for Alzheimer’s disease diagnosis with multi-modality data

Zhang, J., He, X., Liu, Y., Cai, Q., Chen, H., & Qing, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107050 · Cited by 117 · see citing works

Explainable Deep-Learning-Based Diagnosis of Alzheimer’s Disease Using Multimodal Input Fusion of PET and MRI Images

Odusami, M., Maskeliūnas, R., Damaševičius, R., & Misra, S. (2023) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-023-00801-3 · Cited by 116 · see citing works

Data-driven FDG-PET subtypes of Alzheimer’s disease-related neurodegeneration

Levin, F., Ferreira, D., Lange, C., Dyrba, M., Westman, E., Buchert, R., Teipel, S. J., Grothe, M. J., & Initiative, F. T. A. D. N. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00785-9 · Cited by 109 · see citing works

Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions

Elazab, A., Wang, C., Abdel-Aziz, M. H., Zhang, J., Gu, J. J., Górriz, J. M., Zhang, Y., & Chang, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124780 · Cited by 109 · see citing works

Relation-induced multi-modal shared representation learning for Alzheimer’s disease diagnosis

Ning, Z., Xiao, Q., Feng, Q., Chen, W., & Zhang, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3063150 · Cited by 106 · see citing works

Multimodal Fusion-Based Deep Learning Network for Effective Diagnosis of Alzheimer’s Disease

Dwivedi, S., Goel, T., Tanveer, M., Murugan, R., & Sharma, R. (2022) · IEEE Multimedia · doi:10.1109/mmul.2022.3156471 · Cited by 97 · see citing works

Multi-scale enhanced graph convolutional network for mild cognitive impairment detection

Lei, B., Zhu, Y., Yu, S., Hu, H., Xu, Y., Yue, G., Wang, T., Zhao, C., Chen, S., Yang, P., et al. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2022.109106 · Cited by 96 · see citing works

ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease

Shi, Y., Zu, C., Hong, M., Zhou, L., Wang, L., Wu, X., Zhou, J., Zhang, D., & Wang, Y. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108566 · Cited by 96 · see citing works

Alzheimer’s disease diagnosis framework from incomplete multimodal data using convolutional neural networks

Abdelaziz, M., Wang, T., & Elazab, A. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103863 · Cited by 93 · see citing works

Deep learning-based classification of healthy aging controls, mild cognitive impairment and Alzheimer’s disease using fusion of MRI-PET imaging

Rallabandi, V. P. S., & Seetharaman, K. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104312 · Cited by 82 · see citing works

Alzheimer’s disease unveiled: Cutting-edge multi-modal neuroimaging and computational methods for enhanced diagnosis

Mahmood, T., Rehman, A. U., Saba, T., Wang, Y., & Alamri, F. S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106721 · Cited by 79 · see citing works

A hierarchical attention-based multimodal fusion framework for predicting the progression of Alzheimer’s disease

Lu, P., Hu, L., Mitelpunkt, A., Bhatnagar, S., Lu, L. J., & Liang, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105669 · Cited by 73 · see citing works

Deep Multi-Modal Discriminative and Interpretability Network for Alzheimer’s Disease Diagnosis

Zhu, Q., Xu, B., Huang, J., Wang, H., Xu, R., Shao, W., & Zhang, D. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3230750 · Cited by 72 · see citing works

A Meta-Heuristic Multi-Objective Optimization Method for Alzheimer’s Disease Detection Based on Multi-Modal Data

Ismail, W. N., P., F. R. P., & Ali, M. A. S. (2023) · Mathematics · doi:10.3390/math11040957 · Cited by 71 · see citing works

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis

Abdel-Aziz, M. H., Wang, T., Anwaar, W., & Elazab, A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109438 · Cited by 71 · see citing works

Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

Logan, R. A., Williams, B. G., da Silva, M. F., Indani, A., Schcolnicov, N., Ganguly, A., & Miller, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.720226 · Cited by 67 · see citing works

Brain functional network modeling and analysis based on fMRI: a systematic review

Wang, Z., Xin, J., Wang, Z., Yao, Y., Zhao, Y., & Qian, W. (2020) · Cognitive Neurodynamics · doi:10.1007/s11571-020-09630-5 · Cited by 66 · see citing works

Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer

Tang, Y. M., Xiong, X., Tong, G., Yang, Y., & Zhang, H. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01204-4 · Cited by 64 · see citing works

U-net based analysis of MRI for Alzheimer’s disease diagnosis

Fan, Z., Li, J., Zhang, L., Zhu, G., Li, P., Lu, X., Shen, P., Shah, S. A. A., Bennamoun, M., Hua, T., et al. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05983-y · Cited by 61 · see citing works

Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option?

Ferrando, R., & Damián, A. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.629442 · Cited by 58 · see citing works

Reduced Hippocampal Glutamate and Posterior Cingulate N-Acetyl Aspartate in Mild Cognitive Impairment and Alzheimer’s Disease Is Associated with Episodic Memory Performance and White Matter Integrity in the Cingulum: A Pilot Study

Wong, D. Y., Atiya, S., Fogarty, J., Montero‐Odasso, M., Pasternak, S., Brymer, C., BORRIE, M. J., & Bartha, R. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-190773 · Cited by 56 · see citing works

CsAGP: Detecting Alzheimer's disease from multimodal images via dual-transformer with cross-attention and graph pooling

Tang, C., Wei, M., Sun, J., Wang, S., & Zhang, Y. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101618 · Cited by 54 · see citing works

Multimodal transformer network for incomplete image generation and diagnosis of Alzheimer’s disease

Gao, X., Shi, F., Shen, D. G., & Liu, M. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102303 · Cited by 52 · see citing works

A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer’s disease

Choudhury, C., Goel, T., & Tanveer, M. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102415 · Cited by 52 · see citing works

Preclinical Stage Alzheimer's Disease Detection Using Magnetic Resonance Image Scans

Altay, F., Sánchez, G. R., Zhang‐James, Y., Faraone, S. V., Velipasalar, S., & Salekin, A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i17.17772 · Cited by 50 · see citing works

Combined use of Donepezil and Memantine increases the probability of five-year survival of Alzheimer’s disease patients

Yaghmaei, E., Lu, H., Ehwerhemuepha, L., Zheng, J., Danioko, S., Rezaie, A., Sajjadi, S. A., & Rakovski, C. (2024) · Communications Medicine · doi:10.1038/s43856-024-00527-6 · Cited by 47 · see citing works

The Sporadic Early‐onset Alzheimer's Disease Signature Of Atrophy: Preliminary Findings From The Longitudinal Early‐onset Alzheimer's Disease Study (LEADS) Cohort

Touroutoglou, A., Katsumi, Y., Brickhouse, M. J., Zaitsev, A., Eckbo, R., Aisen, P. S. S., Beckett, L. A., Dage, J. L., Eloyan, A., Foroud, T., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13466 · Cited by 47 · see citing works

Hypergraph-regularized multimodal learning by graph diffusion for imaging genetics based Alzheimer’s Disease diagnosis

Wang, M., Shao, W., Huang, S., & Zhang, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102883 · Cited by 46 · see citing works

Patch-based deep multi-modal learning framework for Alzheimer’s disease diagnosis using multi-view neuroimaging

Liu, F., Yuan, S., Li, W., Xu, Q. J., & Sheng, B. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104400 · Cited by 45 · see citing works

HAMMF: Hierarchical attention-based multi-task and multi-modal fusion model for computer-aided diagnosis of Alzheimer’s disease

Liu, X., Li, W., Miao, S., Liu, F., Han, K., & Bezabih, T. T. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108564 · Cited by 39 · see citing works

Multimodal cross enhanced fusion network for diagnosis of Alzheimer’s disease and subjective memory complaints

Leng, Y., Cui, W., Peng, Y., Yan, C., Cao, Y., Yan, Z., Chen, S., Jiang, X., & Zheng, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106788 · Cited by 36 · see citing works

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention

Ye, J., Zeng, A., Pan, D., Zhang, Y., Zhao, J., Chen, Q., & Liu, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3368500 · Cited by 35 · see citing works

Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression

Xiao, R., Cui, X., Qiao, H., Zheng, X., & Zhang, Y. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09738-0 · Cited by 35 · see citing works

Fusion of Brain PET and MRI Images Using Tissue-Aware Conditional Generative Adversarial Network With Joint Loss

Kang, J., Lu, W., & Zhang, W. (2020) · IEEE Access · doi:10.1109/access.2019.2963741 · Cited by 34 · see citing works

Patch-based interpretable deep learning framework for Alzheimer’s disease diagnosis using multimodal data

Zhang, H., Ni, M., Yang, Y., Xie, F., Wang, W., He, Y., Chen, W., & Chen, Z. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107085 · Cited by 34 · see citing works

Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on18F-Florbetapir PET Using ADNI Data

Reith, F. H., Koran, M. E. I., Davidzon, G., & Zaharchuk, G. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6573 · Cited by 32 · see citing works

Pyramid-attentive GAN for multimodal brain image complementation in Alzheimer’s disease classification

Zhang, M., Sun, L., Kong, Z., Zhu, W., Yi, Y., & Yan, F. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105652 · Cited by 30 · see citing works

MACFNet: Detection of Alzheimer's disease via multiscale attention and cross-enhancement fusion network

Tang, C., Xi, M., Sun, J., Wang‎, S., & Zhang, Y. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108259 · Cited by 30 · see citing works

Neuropathological changes associated with aberrant cerebrospinal fluid p-tau181 and Aβ42 in Alzheimer’s disease and other neurodegenerative diseases

Kurihara, M., Matsubara, T., Morimoto, S., Arakawa, A., Ohse, K., Kanemaru, K., Iwata, A., Murayama, S., & Saito, Y. (2024) · Acta Neuropathologica Communications · doi:10.1186/s40478-024-01758-3 · Cited by 29 · see citing works

3D Convolutional Neural Network and Stacked Bidirectional Recurrent Neural Network for Alzheimer’s Disease Diagnosis

Feng, C., Elazab, A., Yang, P., Wang, T., Lei, B., & Xiao, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00320-3_17 · Cited by 29 · see citing works

End-to-end automatic pathology localization for Alzheimer’s disease diagnosis using structural MRI

Cao, G., Zhang, M., Wang, Y., Zhang, J., Han, Y., Xu, X., Huang, J., & Kang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107110 · Cited by 28 · see citing works

Transformer-Based Multimodal Fusion for Early Diagnosis of Alzheimer's Disease Using Structural MRI And PET

Zhang, Y., Sun, K., Liu, Y., & Shen, D. G. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230577 · Cited by 27 · see citing works

MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's DiseasePreprint

Sikka, A., Peri, S. V., Virk, J. S., Niyaz, U., & Bathula, D. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02160 · Cited by 27 · see citing works

Effective Alzheimer’s disease detection using enhanced Xception blending with snapshot ensemble

Mahanty, C., Rajesh, T., Govil, N., Venkateswarulu, N., Kumar, S., Lasisi, A., Islam, S., & Khan, W. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-80548-2 · Cited by 26 · see citing works

Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data

Sun, X., Guo, W., & Shen, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1043626 · Cited by 25 · see citing works

Multimodal fusion diagnosis of Alzheimer’s disease based on FDG-PET generation

Tu, Y., Lin, S., Qiao, J., Zhuang, Y., Wang, Z., & Wang, D. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105709 · Cited by 25 · see citing works

Multi-modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

Avilés-Rivero, A. I., Runkel, C., Papadakis, N., Kourtzi, Z., & Schönlieb, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16437-8_69 · Cited by 25 · see citing works

Innovative Therapeutic Strategies in Alzheimer’s Disease: A Synergistic Approach to Neurodegenerative Disorders

Niazi, S. K., Magoola, M., & Mariam, Z. (2024) · Pharmaceuticals · doi:10.3390/ph17060741 · Cited by 24 · see citing works

Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network

de Vries, B. M., Golla, S. S. V., Ebenau, J. L., Verfaillie, S. C. J., Timmers, T., Heeman, F., Cysouw, M. C. F., van Berckel, B. N. M., van der Flier, W. M., Yaqub, M. M., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05006-3 · Cited by 24 · see citing works

Recent advances in the total synthesis of galantamine, a natural medicine for Alzheimer's disease

Cheng, B., Wang, Q., An, Y., & Chen, F. (2024) · Natural Product Reports · doi:10.1039/d4np00001c · Cited by 23 · see citing works

Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer’s Disease Classification

Odusami, M., Maskeliūnas, R., & Damaševičius, R. (2023) · Brain Sciences · doi:10.3390/brainsci13071045 · Cited by 22 · see citing works

Multi input–Multi output 3D CNN for dementia severity assessment with incomplete multimodal data

Gravina, M., Garcia-Pedrero, A. M., Gonzalo‐Martín, C., Sansone, C., & Soda, P. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102774 · Cited by 21 · see citing works

Multi-view imputation and cross-attention network based on incomplete longitudinal and multimodal data for conversion prediction of mild cognitive impairment

Wang, T., Chen, X., Zhang, X., Zhou, S., Feng, Q., & Huang, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120761 · Cited by 20 · see citing works

Dual Attention Aware Octave Convolution Network for Early-Stage Alzheimer's Disease Detection

Rangaraju, B., Chinnadurai, T., Natarajan, S., & Raja, V. (2024) · Information Technology And Control · doi:10.5755/j01.itc.53.1.34536 · Cited by 20 · see citing works

Alzheimer’s disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction

Cheng, J., Wang, H., Wei, S., Mei, J., Liu, F., & Zhang, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108000 · Cited by 20 · see citing works

A systematic review of the efficacy and safety of anti-amyloid beta monoclonal antibodies in treatment of Alzheimer’s disease

Chhabra, A., Solanki, S., Saravanabawan, P., Venkiteswaran, A., Nimmathota, N., & Modi, N. M. (2024) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2024.2416947 · Cited by 20 · see citing works

Multi-task multi-level feature adversarial network for joint Alzheimer’s disease diagnosis and atrophy localization using sMRI

Han, K., He, M., Yang, F., & Zhang, Y. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac5ed5 · Cited by 19 · see citing works

Is a PET All You Need? A Multi-modal Study for Alzheimer’s Disease Using 3D CNNs

Narazani, M., Sarasúa, I., Pölsterl, S., Lizarraga, A., Yakushev, I. Y., & Wachinger, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_7 · Cited by 19 · see citing works

DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET

Li, Y., Ghahremani, M., Wally, Y., & Wachinger, C. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00021 · Cited by 19 · see citing works

The early-onset Alzheimer’s disease MRI signature: a replication and extension analysis in early-stage AD

Mehta, R. I., Keith, C. M., Teixeira, C. V. L., Worhunsky, P. D., Phelps, H. E., Ward, M., Miller, M. D., Navia, R. O., Pockl, S., Rajabalee, N., et al. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae475 · Cited by 15 · see citing works

A Novel Image Casting and Fusion for Identifying Individuals at Risk of Alzheimer’s Disease Using MRI and PET Imaging

Fakoya, A., & Parkinson, S. (2024) · IEEE Access · doi:10.1109/access.2024.3412850 · Cited by 14 · see citing works

Current status of PET tracers for the early diagnosis of Alzheimer's disease

Li, Y.-Y., Liu, T., Zeng, Q., & Cui, M.-C. (2024) · Trends in Analytical Chemistry (TrAC) · doi:10.1016/j.trac.2024.117546 · Cited by 14 · see citing works

Subcortical tau deposition and plasma glial fibrillary acidic protein as predictors of cognitive decline in mild cognitive impairment and Alzheimer’s disease

Chang, Y. Z., Liu, J., Xu, X., Sun, S., Zhang, J., Zhang, X., Lu, G., Xiao, S., Cao, Y., Wu, R., et al. (2024) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-024-07016-x · Cited by 14 · see citing works

Multi-modal feature selection with anchor graph for Alzheimer's disease

Li, J., Xu, H. L., Yu, H., Jiang, Z., & Zhu, L. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1036244 · Cited by 13 · see citing works

Fusing Multimodal and Anatomical Volumes of Interest Features Using Convolutional Auto-Encoder and Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

Abdelaziz, M., Wang, T.-F., & Elazab, A. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.812870 · Cited by 12 · see citing works

Identification of Mild cognitive impairment based on quadruple GCN model constructed with multiple features from higher-order brain connectivity

Li, Y., Zou, Y., Guo, H., Yang, Y., Li, N., Li, L., & Zhao, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120575 · Cited by 12 · see citing works

Unique transcriptional signatures correlate with behavioral and psychological symptom domains in Alzheimer’s disease

Fisher, D. W., Dunn, J. T., Keszycki, R. M., Rodríguez, G., Bennett, D. A., Wilson, R. S., & Dong, H. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02878-z · Cited by 10 · see citing works

Development of novel rivastigmine derivatives as selective BuChE inhibitors for the treatment of AD

Li, Y., Zhang, Q., Wang, X., Liu, Z., Chen, H., Xu, Y., Xu, Y., Zhang, W., Du, Y., Tan, Z., et al. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.108245 · Cited by 10 · see citing works

Clinical evidence of hyperbaric oxygen therapy for Alzheimer’s disease: a systematic review and meta-analysis of randomized controlled trials

Lin, G., Zhao, L., Lin, J., Li, X., & Xu, L. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1360148 · Cited by 9 · see citing works

MULTIMODAL 2.5D CONVOLUTIONAL NEURAL NETWORK FOR DIAGNOSIS OF ALZHEIMER'S DISEASE WITH MAGNETIC RESONANCE IMAGING AND POSITRON EMISSION TOMOGRAPHY

Zhang, X., Lin, W.-M., Xiao, M., & Ji, H. (2021) · Electromagnetic waves · doi:10.2528/pier21051102 · Cited by 8 · see citing works

Single-molecule imaging of Tau reveals how phosphorylation affects its movement and confinement in living cells

Padmanabhan, P., Kneynsberg, A., Cruz, E., Briner, A., & Götz, J. (2024) · Molecular Brain · doi:10.1186/s13041-024-01078-6 · Cited by 7 · see citing works

Diagnosis Framework for Probable Alzheimer’s Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features

Zhang, C., Lei, X., Ma, W., Long, J., Long, S., Chen, X., Luo, J., & Tao, Q. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-230703 · Cited by 6 · see citing works

Tensor Completion for Alzheimer's Disease Prediction From Diffusion Tensor Imaging

Gou, Y., Liu, Y., He, F., Hunyadi, B., & Zhu, C. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3365131 · Cited by 6 · see citing works

Conversion-aware forecasting of Alzheimer’s disease via featurewise attention

Karasu, E., & Baytaş, İ. M. (2025) · Pattern Analysis and Applications · doi:10.1007/s10044-025-01447-4 · Cited by 6 · see citing works

Hybrid Multimodality Fusion with Cross-Domain Knowledge Transfer to Forecast Progression Trajectories in Cognitive Decline

Yu, M., Liu, Y., Wu, J., Bozoki, A., Qiu, S., Yue, L., & Liu, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47425-5_24 · Cited by 6 · see citing works

Dimethysiloxane polymer for the effective transdermal delivery of donepezil in Alzheimer's disease treatment

Lee, J., Ju, I. G., Lim, Y., Kim, J. H., Lee, S.-M., Choi, Y., Oh, M. S., Kim, J., & Kim, D. (2024) · Biomaterials Science · doi:10.1039/d4bm01368a · Cited by 5 · see citing works

Dihydroergotamine and Bromocriptine: Potential Drugs for the Treatment of Major Depressive Disorder and Alzheimer’s Disease Comorbidity

Fu, M., Wang, Q., Gao, L., Ma, Q., & Wang, J. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04416-w · Cited by 4 · see citing works

Alzheimer's Disease Detection Model Based on Multimodal Data Early Fusion of Medical Neuroimaging

Wu, S., Qin, S., Wang, K., Yang, S., & Zhang, S. (2023) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys60770.2023.00116 · Cited by 3 · see citing works

Hybrid Fusion Approach for Alzheimer’s Disease Progression Employing IHS and Wavelet Transform

Hussein, D. Y., Makkey, M. Y., & Abdelrahman, S. A. (2024) · Menoufia Journal of Electronic Engineering Research · doi:10.21608/mjeer.2023.234331.1081 · Cited by 2 · see citing works

Vision and convolutional transformers for Alzheimer’s disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps

Afifi, I., Elgendy, M. A., Abdelfatah, M., & El–Sappagh, S. (2025) · Brain Informatics · doi:10.1186/s40708-025-00286-7 · Cited by 15 · see citing works

Alzheimer’s diagnosis from EEG with reliable probabilities: subject-wise, leakage-free evaluation and isotonic calibration

Shamsi, H. (2025) · Journal of Engineering and Applied Science · doi:10.1186/s44147-025-00821-7 · Cited by 8 · see citing works

Multimodal lightweight neural network for Alzheimer's disease diagnosis integrating neuroimaging and cognitive scores

Gupta, B., Jegannathan, G. K., Alam, M. S., Yogi, K. S., Ramesh, J. V. N., Sowmya, V. J., & Bayhan, İ. (2025) · Neuroscience Informatics · doi:10.1016/j.neuri.2025.100218 · Cited by 8 · see citing works

Data-driven subtyping of early Parkinson’s disease via mutual cross-attention fusion of EEG and dual-task gait features

Wang, D., Shi, Y., Pang, J., Zhu, X., Meng, L., & Ming, D. (2026) · npj Parkinson s Disease · doi:10.1038/s41531-026-01258-2 · Cited by 5 · see citing works

Counterfactual, Longitudinal, and Multimodal Explainable AI for MRI-Based Alzheimer’s Diagnosis: A Structured Review

Farha, R., Ojeme, B., Khalifa, F., & Rahman, M. M. (2026) · Journal of dementia and Alzheimer's disease · doi:10.3390/jdad3020026 · Cited by 4 · see citing works

RaSizeNet: An interpretable multimodal deep learning model integrating SERS spectra and inertial microfluidic size distribution for precise detection of complex pathogens

Su, G., Yang, W., Liu, G., Yang, X., Zhang, G., Wu, B., Dang, L., Wei, H., & Wang, Z. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.141491 · Cited by 2 · see citing works

Multimodal Deep Learning for Early Prediction of Neurodegenerative Diseases: A Comprehensive Review

Gouthami, P., & Krishnamoorthi, M. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438980 · Cited by 2 · see citing works

SPECT and PET imaging of Alzheimer’s disease revisited: from biomarkers to artificial intelligence-based prediction

Tsougos, I., Valotassiou, V. J., Tsivaka, D., Σάτρα, Μ., Angelidis, G., Papatriantafyllou, J. D., Panagiotidis, E., Dardiotis, E., Hadjigeorgiou, G. M., & Georgoulias, P. (2026) · Annals of Nuclear Medicine · doi:10.1007/s12149-026-02194-0 · Cited by 2 · see citing works

The Explainable Attention-Guided Multimodal Ensemble Framework for Alzheimer’s Disease Prediction and Severity Estimation

Laxmikantha, K., & Ashokkumar, P. S. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.18269 · Cited by 3 · see citing works

Advancing Dementia Prediction: A Robust CNN Framework for Early Diagnosis

Reza, M. F., & Kelly, A. (2025) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc67473.2025.00103 · Cited by 1 · see citing works

Causal-aware spatio-temporal hypergraph learning for early health risk prediction in community elderly care

Fang, Z. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.03.032 · Cited by 1 · see citing works

Contrastive local and global representation learning for Alzheimer’s disease diagnosis

Yin, J., Liao, X., Peng, D., Wu, F., & Liao, B. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110573 · Cited by 1 · see citing works

Study on real-time forecast of tunnel fuel and electric vehicle fire states based on multimodal fusion learning

Guo, C., Chen, J., Kan, D., Lin, X., Yan, Z., & Zhang, Y. (2026) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2026.107797 · Cited by 1 · see citing works

Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task

Camastra, C., Pelagi, A., Quattrone, A., & Sarica, A. (2026) · Brain Sciences · doi:10.3390/brainsci16040405 · Cited by 1 · see citing works

Deep Learning-Based Multimodal Neuroimaging Framework for Brain Lesion Detection and Prognostic Biomarker Analysis

Vinod, C., Ananthan, A., Shehras, V. U., Praseena, K., Menon, A. G., & Sreebala, P. (2025) · International Conference on Power, Instrumentation, Control and Computing (PICC) · doi:10.1109/picc67314.2025.11291450

Multimodal Deep Learning for Alzheimer’s Diagnosis Using PET and MRI

Jayman, Z. S., & Lakmal, H. K. I. S. (2025) · SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) · doi:10.1109/slaai-icai68534.2025.11318489

Digital siblings unveil distinct molecular and clinical subtypes in Alzheimer's disease via omics-driven profiling

Bengs, B. D., Swerdlow, R. H., Burns, J., Slawson, C. M., McCoy, M., & Sardiu, M. E. (2026) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2026.125812

A novel lightweight multi-attention fusion network for Alzheimer’s disease detection

Sheng, J., Huang, R., Zhang, Q., Li, T., Miao, M., & Lu, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110183

Advances in the Application of Multi-Modal Image Fusion in the Diagnosis of Neurological Diseases—AI-Based Technical Paradigms and Clinical Translation

刘, 子. (2026) · Advances in Clinical Medicine · doi:10.12677/acm.2026.1641356

Prediction and Detection of Dementia Disease - A Review

Sharma, Y., Shrivastva, A., & Bindal, A. K. (2026) · International Conference Big Data and Machine Learning · doi:10.1109/icbdml68582.2026.11544191

Bridging the islands of innovation: A machine-assisted Semantic–Bibliometric review and conceptual roadmap for closed-loop digital dementia care

Wang, H., & Zhang, X. (2026) · Digital Health · doi:10.1177/20552076261479403

Multimodal Retinal Imaging for the Detection of Early Alzheimer’s Disease: Combining Biochemical, Structural, and Vascular Biomarkers

Ghesquiere, M., Christinaki, E., Lemmens, S., Eijgen, J. V., Beeckmans, L., Ghinis, A., Jacobs, T., Keer, K. V., Wehbi, Z., Groef, L. D., et al. (2026) · Bioengineering · doi:10.3390/bioengineering13091041

Diagnosis of neurodegenerative diseases based on molecular imaging

Quan, L., Lang, M., Yue, J., Zhu, J., Xu, J., Wang, K., Huang, H., Song, Q., Zhao, Y., Zhu, Z., et al. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.180865

Multimodal Transformer Fusion of Clinical Information, Medical Text, and Pituitary MRI 2.5D Deep Learning Features for Differentiating Growth Hormone Deficiency and Idiopathic Short Stature

Zhang, K., Jia, J., Lin, J., Hu, J., Zhu, Y., Wu, Z., Yang, L., & Huang, C. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.08.015

LHMM-Net: A Lightweight Hybrid Multi-scale Mamba Network with 2.5D Representation for Early Alzheimer' s Disease Diagnosis

Yang, H., & Kong, W. (2026) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3809986.3810088

Progressive GAN-Augmented CNN for Multi-Class Dementia Classification With Severe Class Imbalance

Reza, M. F. (2026) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit68936.2026.11670360

Multimodal Data Fusion and Intelligent Early Warning for Pet Health

Hongxin, S., Zhang, X., Yu, J., Tang, J., Mao, X., & Jiao, J. (2026) · International Conference on Software Engineering and Computer Applications · doi:10.1145/3829641.3829756

Advances in Multimodal Deep Learning Models for Early Alzheimer's Disease Detection: A Survey

Zhang, J., Fei, Z., Zhou, W., & Fei, M. (2026) · International Conference on Machine Learning and Intelligent Computing · doi:10.1145/3829441.3829478

IFMA-Net: Interactive Feature Modulated Attention Network for Accurate Identification of Cotton Pests and Diseases from Field Images

Song, L., Chen, Z., Liu, C., Guo, Y., & Wang, D. (2026) · Sensors · doi:10.3390/s26196026

An enhanced grey wolf optimizer-based Facebook artificial intelligence similarity search for Alzheimer’s disease diagnosis

Li, T., Sheng, J., Zhang, Q., Gong, Z., Wu, M., Huang, R., & Lu, Y. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.09.034

A Study on the Application of Multimodal MRI Deep Learning in the Early Detection of Osteonecrosis of the Femoral HeadPreprint

Wang, X., Liu, K., Liwei, Z., Chao, M., Lingting, M., Jianming, L., Xiao, H., Guangwen, L., Yuju, C., & Jianzhong, W. (2026) · Research Square · doi:10.21203/rs.3.rs-11043385/v1

A Review of MRI-PET Fusion for Alzheimer’s Disease Diagnosis

秦, 傲. (2026) · Journal of Image and Signal Processing · doi:10.12677/jisp.2026.154042

Multimodal Fusion-Based Deep Learning Network for Effective Diagnosis of Alzheimer’s Disease

Dwivedi, S., Goel, T., Tanveer, M., Murugan, R., & Sharma, R. (2022) · IEEE Multimedia · doi:10.1109/mmul.2022.3156471 · Cited by 97 · see citing works

Multi-modal Multi-Level Neuroimaging Fusion with Modality-Aware Mask-Guided Attention and Deep Canonical Correlation Analysis to Improve Dementia Risk Prediction

Singh, S. S. (2025) · VTechWorks (Virginia Tech)

An advanced multimodal image fusion model for accurate detection of Alzheimer's disease using MRI and PET

Ansari, A. S., Mohammadi, M. S., Cattani, C., & Tassaddiq, A. (2025) · Frontiers in Medical Technology · doi:10.3389/fmedt.2025.1699821 · Cited by 8 · see citing works

An effective multimodal image fusion method using MRI and PET for Alzheimer’s disease diagnosis

Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., & Shen, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.637386 · Cited by 178 · see citing works

Deep Learning for Alzheimer's Disease Detection using Multimodal MRI-PET Fusion

Suma, K. V., Raghavan, D., & Ganesh, P. N. (2022) · International Conference on Circuits, Control, Communication and Computing (I4C) · doi:10.1109/i4c57141.2022.10057623 · Cited by 9 · see citing works