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Research

29,780 Alzheimer's and dementia studies in plain English, each scored out of 100.

Find studies

Showing 51–75 of 76 studies in Digital health tools published 2021 (page 3 of 4) · 29,780 in the whole library · How the 0–100 score works

Download bibliography of these 76 studies, in this order: BibTeX (.bib) · RIS (.ris)

Browse by subject, author or institution

Each number is how many studies that link shows, with your other filters kept.

Other subjects

Authors

Names as each record prints them; the same person can appear under two spellings.

Institutions

From OpenAlex; 15,255 studies have no institution on record and are not counted here.

What “Digital health tools” covers

Digital health tools — Apps, wearables, online tests and other technology used to detect, monitor or support people with dementia.

A note on reading research. One study is rarely the final word.

We try to note the size of a study, whether it was in humans or animals, and how early-stage the finding is. If a summary here doesn't make that clear, treat it with extra caution and check the original source.

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

✓ Peer-reviewed alzhub usefulness score 47 out of 100. NeuroImage. Clinical 2021-06-04

This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). 2 key findings →

Participants: 199 Evidence: moderate

Full summary, score breakdown and related studies → Original source Free full text

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer's disease: Machine learning approaches

✓ Peer-reviewed alzhub usefulness score 47 out of 100. Journal of psychopharmacology (Oxford, England) 2021-02-15

This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. 4 key findings →

Participants: 21 Evidence: low

Full summary, score breakdown and related studies → Original source Full text may need a subscription

Cortical Thickness from MRI to Predict Conversion from Mild Cognitive Impairment to Dementia in Parkinson Disease: A Machine Learning-based Model

✓ Peer-reviewed alzhub usefulness score 46 out of 100. Radiology 2021-05-25

Cortical thickness from MRI helped predict conversion from mild cognitive impairment to dementia in Parkinson disease at an individual level, with improved performance when integrated with clinical variables. © RSNA, 2021 Online supplemental material is available for this article. 2 key findings →

Participants: 75 Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders

✓ Peer-reviewed alzhub usefulness score 45 out of 100. Frontiers in neuroscience 2021-03-31

Amyloid-beta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) are considered core biomarkers for the diagnosis of Alzheimer's disease (AD). 5 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source Free full text

GABAergic dysfunction, neural network hyperactivity and memory impairments in human aging and Alzheimer's disease

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Seminars in cell & developmental biology 2021-02-09

In this review, we focus on the potential role of the γ-aminobutyric acidergic (GABAergic) system in age-related episodic memory impairments in humans, with a particular focus on Alzheimer's disease (AD). 4 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source

Bidirectional Mapping of Brain MRI and PET With 3D Reversible GAN for the Diagnosis of Alzheimer's Disease

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Frontiers in neuroscience 2021-04-09

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

Potential value and impact of data mining and machine learning in clinical diagnostics

✓ Peer-reviewed alzhub usefulness score 42 out of 100. Critical reviews in clinical laboratory sciences 2021-03-19

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. 1 key finding →

Evidence: low

Full summary, score breakdown and related studies → Original source Full text may need a subscription

ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model

✓ Peer-reviewed alzhub usefulness score 40 out of 100. IEEE transactions on medical imaging 2021-03-02

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging technique that has been increasingly used to image the retinal vasculature at capillary level resolution. 2 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source

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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 29,780 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 29,780 records, built mostly from automated pulls of Europe PMC (63.8% of the library) and OpenAlex (17.1%), 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.

1,037 records (3.5% of the library) are not peer-reviewed — 964 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 →

Newest studies, as a dated timeline →    Atom feed