Using pre-trained language models can improve AD prediction. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Research
27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.
46/100
Models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66-0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217). 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
The self-supervised ADRET model had superior accuracy when compared with ADVAS, in both UK Biobank (98.27% vs 77.20%; P<.001) and our institutional testing data sets (98.90% vs 94.17%; P=.04). 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI. 4 key findings →
Participants: 509 Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD. 1 key finding →
Participants: 167 Evidence: low
Full summary, score breakdown and related studies → Original source
The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. 1 key finding →
Participants: 116 Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming abnormal, the pathophysiology of which is very complex and largely unknown. 2 key findings →
Design: cross-sectional Evidence: low
Full summary, score breakdown and related studies → Original source
45/100
Older individuals infected by Human Immunodeficiency Virus (HIV) are at risk for developing HIV-Associated Neurocognitive Disorder (HAND), i.e., from reduced cognitive functioning similar to HIV-negative individuals with Mild Cognitive Impairment (MCI) or to Alzheimer's Disease (AD) if more severely affected. 1 key finding →
Evidence: moderate
Full summary, score breakdown and related studies → Original source
Recently, deep-learning-based approaches have been proposed for the classification of neuroimaging data related to Alzheimer's disease (AD), and significant progress has been made. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
45/100
Our results show that DICE-net achieved an accuracy of 83.28% in the AD-CN problem using Leave-One-Subject-Out validation, outperforming several baseline models, and achieving good generalization performance. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
45/100
Alzheimer's Disease (AD) is the most common cause of dementia globally. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying white matter tracts. 2 key findings →
Participants: 28 Evidence: low
Full summary, score breakdown and related studies → Original source
Multivariate pattern analysis and statistical machine learning techniques are attracting increasing interest from the neuroimaging community. 2 key findings →
Participants: 77 Evidence: low
Full summary, score breakdown and related studies → Original source
Combining multi-modality data for brain disease diagnosis such as Alzheimer's disease (AD) commonly leads to improved performance than those using a single modality. 2 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Brain MR images are the most suitable method for detecting chronic nerve diseases such as brain tumors, strokes, dementia, and multiple sclerosis. 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
We present iCluster, a fast and efficient algorithm that clusters a set of images while co-registering them using a parameterized, nonlinear transformation model. 3 key findings →
Participants: 15 Evidence: low
Full summary, score breakdown and related studies → Original source
From across this tag
9 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 9. The same 9 appear here every time.
- Retinal image analytics detects white matter hyperintensities in healthy adults
- Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network
- Deep Convolutional Neural Networks and Transfer Learning for Measuring Cognitive Impairment Using Eye-Tracking in a Distributed Tablet-Based Environment
- Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review
- Artificial Intelligence in Alzheimer’s Disease: Evidence Maturity and Clinical Translation Across the Care Pathway
- Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification
- Automated quantification of cerebral microbleeds for ARIA-H monitoring in Aging and Alzheimer’s Disease: A multicenter deep learning validation
- Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination
- Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
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 27,380 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 27,380 records, built mostly from automated pulls of Europe PMC (60.6% of the library) and OpenAlex (18.6%), 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.
1,032 records (3.8% 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 →