SynthesisSensors (Basel, Switzerland)2021
Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.European radiology · 2023Trial
- Prediction of Antipsychotic Drug Doses for BPSD in Alzheimer's Disease Using Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach.Artificial intelligence in medicine · 2026Article
- Alzheimer's disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning.Frontiers in neuroscience · 2026Article
- Personalized prevention of neurodegenerative diseases: scoping review and evidence gap map.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- A novel approach hybrid of ensemble learning and 3-D CNN mechanism: early-stage diagnosis of Alzheimer's disease using EEG signals.Scientific reports · 2025Article
- Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning.Brain and behavior · 2025Article
- Transfer Learning and Neural Network-Based Approach on Structural MRI Data for Prediction and Classification of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.Biomedicines · 2025Review
- Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.Frontiers in neuroinformatics · 2025Review
- Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization.Diagnostics (Basel, Switzerland) · 2024Article
- Optimising brain age estimation through transfer learning: A suite of pre-trained foundation models for improved performance and generalisability in a clinical setting.Human brain mapping · 2024Article
- Classification and transfer learning of sleep spindles based on convolutional neural networks.Frontiers in neuroscience · 2024Article
- Implications of Convolutional Neural Network for Brain MRI Image Classification to Identify Alzheimer's Disease.Parkinson's disease · 2024Article
- A robust class decomposition-based approach for detecting Alzheimer's progression.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA.Healthcare (Basel, Switzerland) · 2023Article
- Navigating Early Alzheimer's Diagnosis: A Comprehensive Review of Diagnostic Innovations.Cureus · 2023Review
- Repurposing drugs against Alzheimer's disease: can the anti-multiple sclerosis drug fingolimod (FTY720) effectively tackle inflammation processes in AD?Journal of neural transmission (Vienna, Austria : 1996) · 2023Review
- Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review.Bioengineering (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.