Combining multi-modality brain data for disease diagnosis commonly leads to improved performance. 1 key finding →
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.
In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic resonance imaging (MRI) have attracted increasing attention since these two tasks are highly correlated. 1 key finding →
Participants: 1,984 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
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
However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for the diagnosis of AD and MCI. 4 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
40/100
For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. 3 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
In recent years, there has been a great interest in computer-aided diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
Many machine learning and pattern classification methods have been applied to the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). 1 key finding →
Evidence: low
Full summary, score breakdown and related studies → Original source
Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). 5 key findings →
Evidence: low
Full summary, score breakdown and related studies → Original source
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 →