Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Research
27,780 Alzheimer's and dementia studies in plain English, each scored out of 100.
Recent research has linked systemic metabolic disorders to cognitive decline and dementia risk, including Alzheimer's. This is suspected to be due to lifestyle-related vascular impairments from atherosclerosis and other factors, such as malnutrition and anaemia. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Digital biomarkers are of growing interest in the field of Alzheimer's Disease (AD) research. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
43/100
Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer’s disease is the fifth-leading cause of death for adults over the age of 65. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The importance of early Alzheimer's Disease screening is becoming more apparent, given the fact that there is no way to revert the patient's status after the onset. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
The drug development pipeline for a new compound can last 10-20 years and cost over $10 billion. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The Personalized Multi-Domain Digital Platform for Dementia Prevention (PM(DP)*2) is a conceptualized evidence-based initiative designed to reduce dementia burden through tailored interventions. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy. 5 key findings →
Evidence: moderate
Full summary, score breakdown and related studies → Original source
The level of prediction error in the brain age estimation frameworks is associated with the authenticity of statistical inference on the basis of regression models. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
41/100
Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. 1 key finding →
Evidence: moderate
Full summary, score breakdown and related studies → Original source
In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's disease is an incurable, progressive neurological brain disorder. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The proposed hi-GCN method performs the graph embedding learning from a hierarchical perspective while considering the structure in individual brain network and the subject's correlation in the global population network, which can capture the most essential embedding features to improve the classification performance of disease diagnosis. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's disease (AD) is a progressive brain disease. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
The following intersection over union values were obtained for the various slices in the verification dataset: frontal lobe axial/sagittal: 0.733/0.804; posterior cingulate cortex and precuneus coronal/sagittal: 0.661/0.726; lateral temporal lobe axial/coronal: 0.864/0.892; parietal lobe axial/coronal: 0.542/0.759; and striatum axial/sagittal: 0.679/0.752. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Artificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
41/100
Alzheimer's disease (AD) remains a global health challenge in the 21st century due to its increasing prevalence as the major cause of dementia. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
From across this tag
10 of the 205 studies in this tag, picked by a fixed rule (a hash of each study's address), not by score or date; each page of this list shows a different 10. The same 10 appear here every time.
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease
- Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly
- Organ aging signatures in the plasma proteome track health and disease
- Interpretable Frequency-Aware Deep Learning for Multi-Class EEG Classification of Neurological Disorders
- Visual interpretation of [18F]Florbetaben PET supported by deep learning-based estimation of amyloid burden
- Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)
- Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline
- Deep learning-based feature representation for AD/MCI classification
- Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
- Deep residual inception encoder-decoder network for amyloid PET harmonization
Where “Free full text” comes from
Free full text links and the Full text may need a subscription label come from Unpaywall, Europe PMC and OpenAlex records of free copies, checked 10 Oct 2026. A study with neither is unknown, not paywalled. Show only studies that are free to read
How the 0–100 score works — and why some entries are capped
This is alzhub's own score. It is not an official metric, not an impact factor, and no journal, publisher or ranking body endorses it. We built it so that a reader who is not a scientist can tell a landmark trial from a conference abstract without having to already know the difference. Every point comes from one of 20 components, all of them printed on the study itself, so you can disagree with our arithmetic rather than having to trust the total.
- Journal tier up to 10 pts
- Journal's citation record up to 4 pts
- Study design up to 12 pts
- Strength of evidence up to 5 pts
- Peer-review validation up to 10 pts
- How recent up to 5 pts
- Sample size up to 4 pts
- Effect reported up to 3 pts
- Studied in people up to 3 pts
- Randomised / blinded up to 2 pts
- Times cited up to 7 pts
- Citations per year up to 5 pts
- Impact for its field and year up to 6 pts
- Cited by studies here up to 6 pts
- Still being cited up to 4 pts
- Senior author's track record up to 4 pts
- Institution's track record up to 3 pts
- Multi-centre collaboration up to 3 pts
- Indexed in PubMed up to 2 pts
- Free to read up to 2 pts
Journals are placed in ordered tiers, never given a numeric impact factor — we could not verify current impact factors from a source we control, and an invented one would be worse than none. Tier 1 is the top-tier general journals (NEJM, The Lancet, JAMA, Nature, Science, Nature Medicine); tier 2 is the leading specialty journals; tier 3 is every other peer-reviewed journal. A separate part scores how much the journal's own papers are cited, which tells the tier-3 journals apart.
Two rules we don't bend. First, we never fill in a missing value: if a record does not state a sample size or a design, that component scores zero, and if we simply have no data for a part, such as a citation count, that part is left out and the study is scored on the parts we could check. The study card says which. Second, anything that is not a peer-reviewed article is capped at 16/100 and carries a warning chip — so a conference abstract or a company press release can never outrank a peer-reviewed paper, no matter how good it looks.
We rank a rigorous null result — a good trial showing that something does not work — above an early positive finding. Someone deciding whether to buy a supplement is served by that, and the rigour is already counted by the design and journal components.
The score is computed from the published data alone and is reproducible: the same file always yields the same numbers.
See the whole rubric drawn out → — four charts covering all 27,780 studies: how the scores are spread and where the cap bites, which of the 20 components the library is strong and weak on, which subjects rest on stronger evidence, and what kinds of study each subject is actually built from.
What this library is — and isn't
This is a large sample of the Alzheimer's literature, not an exhaustive index of it. It holds 27,780 records, built mostly from automated pulls of Europe PMC (61.1% of the library) and OpenAlex (18.3%), plus smaller pulls from PubMed, Crossref and medRxiv. If a study, a drug, or a finding isn't here, that is not evidence it doesn't exist in the literature — it may simply not have been pulled in yet.
1,034 records (3.7% of the library) are not peer-reviewed — 962 of those are preprints posted before any journal review, and the rest are conference abstracts, regulatory notices and reports. Every study card on this page states its status plainly, and the score above is capped for anything that isn't a peer-reviewed article.
See the full, machine-readable breakdown of this library's coverage →