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Research

30,980 Alzheimer's and dementia studies in plain English, each scored out of 100.

Find studies

Showing 126–150 of 165 studies in Digital health tools · tagged Diagnosis (page 6 of 7) · 30,980 in the whole library · How the 0–100 score works

Download bibliography of these 165 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; 16,452 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.

Artificial intelligence in atrial fibrillation – Timely diagnosis, risk assessment and personalized management

✓ Peer-reviewed alzhub usefulness score 38 out of 100. Indian Pacing and Electrophysiology Journal 2026-01-27

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia worldwide and is associated with substantial morbidity and mortality, including stroke, systemic embolism, heart failure, and dementia. 1 key finding →

Evidence: low

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

Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

✓ Peer-reviewed alzhub usefulness score 38 out of 100. IEEE journal of biomedical and health informatics 2017-01-19

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. 1 key finding →

Evidence: low

Full summary, score breakdown and related studies → Original source

Intrusions and perseverations

✓ Peer-reviewed alzhub usefulness score 36 out of 100. Brain and language 1984-09-01

Twenty-two subjects with presumed senile dementia of the Alzheimer's type (SDAT), twenty-two with dementia other than SDAT, twenty with aphasia, and seventeen healthy controls over 50 years of age were examined for intrusions and perseverations. 3 key findings →

Evidence: low

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

Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation

✓ Peer-reviewed alzhub usefulness score 36 out of 100. Diagnostics 2025-01-17

Utilizing clinical data, the highest ROC AUC scores attained were 0.989, 0.927, and 0.907 for the identification of the CN, MCI, and dementia stages, respectively. 5 key findings →

Evidence: low

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

Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease

✓ Peer-reviewed alzhub usefulness score 36 out of 100. IEEE transactions on bio-medical engineering 2014-11-20

The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. 1 key finding →

Evidence: low

Full summary, score breakdown and related studies → Original source

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer's Disease

✓ Peer-reviewed alzhub usefulness score 36 out of 100. International journal of neural systems 2016-04-04

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. 2 key findings →

Evidence: low

Full summary, score breakdown and related studies → Original source

Leveraging Artificial Intelligence in the Diagnosis of Neurodegenerative Disorders (Alzheimer's and Dementia) and in Managing Medication Schedules"

✓ Peer-reviewed alzhub usefulness score 35 out of 100. Damanhour Journal of Intelligent Systems and Informatics 2025-06-01

The Alzheimer's disease (AD) is recognized as one of the most common neurodegenerative causes of dementia worldwide, having been characterized by progressive cognitive decline for the most part. 2 key findings →

Participants: 461 Evidence: low

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

‘Machine Learning’ multiclassification for stage diagnosis of Alzheimer’s disease utilizing augmented blood gene expression and feature fusion

✓ Peer-reviewed alzhub usefulness score 35 out of 100. Discover Applied Sciences 2025-06-12

Three blood gene expression datasets of high dimensionality and low sample size (HDLSS) have been utilized in this study, with one dataset exhibiting significant class imbalance. 5 key findings →

Evidence: low

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

Enhanced in silico QSAR-based screening of butyrylcholinesterase inhibitors using multi-feature selection and machine learning

✓ Peer-reviewed alzhub usefulness score 34 out of 100. SAR and QSAR in environmental research 2025-02-01

Butyrylcholinesterase inhibition offers one of the formulated solutions to tackle the aggravating symptoms of dementia that downgrades to cholinergic neuronal loss in Alzheimer’s disease. 1 key finding →

Evidence: low

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

Agentic artificial intelligence in cognitive screening: A translational roadmap for dementia care

✓ Peer-reviewed alzhub usefulness score 33 out of 100. Journal of Alzheimer s Disease Reports 2025-01-01

Conventional dementia screening tools, whether paper tests or static artificial intelligence (AI) models, capture only a snapshot of cognition and need to be adminstered periodically. 5 key findings →

Evidence: low

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

Reimagining dementia screening: A stakeholder-informed perspective on artificial intelligence, digital biomarkers, and real-world implementation

✓ Peer-reviewed alzhub usefulness score 33 out of 100. Journal of Alzheimer s Disease Reports 2025-01-01

The approval of disease-modifying treatments for Alzheimer's disease demands a rethinking of cognitive screening. 4 key findings →

Evidence: low

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

Primary Care Physician Perspectives on Artificial Intelligence for Dementia Screening: A Qualitative Study

✓ Peer-reviewed alzhub usefulness score 33 out of 100. AJOB Empirical Bioethics 2026-01-02

Participants reported routine screening for cognitive impairment only in the context of the Medicare annual wellness visit and generally avoided routine screening because of limited treatment options to support patients, such as poor access to geriatrics/neuropsychiatric services. 5 key findings →

Evidence: low

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

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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 30,980 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 30,980 records, built mostly from automated pulls of Europe PMC (65.2% of the library) and OpenAlex (16.4%), 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.

1,040 records (3.4% of the library) are not peer-reviewed — 965 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    Where this research is being discussed: events