ArticleBrain informatics2023
A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer's disease.
Article in Brain informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it, 128 citations in OpenAlex.
- The future of Alzheimer's disease risk prediction: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
- The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.Frontiers in neurology · 2024Pooled it
- Article
- Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts.Research square · 2026Article
- Use of Machine Learning to Identify Markers of Risk for Fragile X-Associated Tremor/Ataxia Syndrome: A Preliminary Analysis.Annals of neurology · 2026Article
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- Comparative Analysis of Deep Learning Techniques in Alzheimer's Disease Diagnosis: Trends, Challenges, and Future Directions.MethodsX · 2026Review
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Real-World Multimodal Machine Learning for Risk Enrichment Across the Alzheimer's Disease Spectrum.Journal of clinical medicine · 2026Article
- Resolving Heterogeneity in the Diagnosis of Alzheimer's Disease and its Progression Using Multimodal Data.Journal of molecular neuroscience : MN · 2026Article
- Functional and Clinical: An Explainable Deep Learning Model for Multimodal Alzheimer's Disease Classification.Brain and behavior · 2026Article
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- TriFusion-ADFormer: a deep learning framework for early Alzheimer's disease detection using MRI and cognitive metrics.Frontiers in artificial intelligence · 2026Article
- VEA-net: vascular enhancement attention with dual-backbone multi-task learning for comprehensive ROP management across multi-center datasets.Frontiers in cell and developmental biology · 2026Article
- Metaheuristic-driven dual-layer model for classifying Alzheimer's disease stages.Frontiers in computational neuroscience · 2026Article
- A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- Alzheimer disease predicting from clinical and MRI data using DeepALZNET dual pathway framework.Scientific reports · 2025Article
- Fusion-ADiNet: a multi-level framework for enhanced diabetes and Alzheimer's disease detection using chimp-whale fusion estimation.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient will convert to AD (namely MCI converter MCI-C) or not (namely MCI non-converter MCI-NC), during early diagnosis. There are two modalities, positron emission tomography (PET) and magnetic resonance image (MRI), used by a physician for the diagnosis of Alzheimer's disease. Machine learning and deep learning perform exceptionally well in the field of computer vision where there is a requirement to extract information from high-dimensional data. Researchers use deep learning models in the field of medicine for diagnosis, prognosis, and even to predict the future health of the patient under medication. This study is a systematic review of publications using machine learning and deep learning methods for early classification of normal cognitive (NC) and Alzheimer's disease (AD).This study is an effort to provide the details of the two most commonly used modalities PET and MRI for the identification of AD, and to evaluate the performance of both modalities while working with different classifiers.
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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 Socratic graph.