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Research

27,380 Alzheimer's and dementia studies in plain English, each scored out of 100.

Find studies

Showing 1–20 of 20 studies in Digital health tools in Scientific reports · 27,380 in the whole library · How the 0–100 score works

Download bibliography of these 20 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; 12,859 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.

A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction

✓ Peer-reviewed alzhub usefulness score 67 out of 100. Scientific reports 2020-11-23

Data collected from clinical trials and cohort studies, such as dementia studies, are often high-dimensional, censored, heterogeneous and contain missing information, presenting challenges to traditional statistical analysis. 1 key finding →

Design: cohort study Participants: 1,037 Evidence: moderate

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

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation

✓ Peer-reviewed alzhub usefulness score 62 out of 100. Scientific reports 2018-03-07

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. 5 key findings →

Design: longitudinal study Participants: 473 Evidence: moderate

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

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

✓ Peer-reviewed alzhub usefulness score 56 out of 100. Scientific reports 2020-11-26

Alzheimer's Disease is a complex, multifactorial, and comorbid condition. 1 key finding →

Design: longitudinal study Evidence: moderate

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

Diagnostic value of digital clock drawing test in comparison with CERAD neuropsychological battery total score for discrimination of patients in the early course of Alzheimer's disease from healthy individuals

✓ Peer-reviewed alzhub usefulness score 54 out of 100. Scientific reports 2019-03-05

Our findings suggest that dCDT is a suitable screening tool to identify early cognitive dysfunction. 1 key finding →

Participants: 381 Evidence: low

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

Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets

✓ Peer-reviewed alzhub usefulness score 51 out of 100. Scientific reports 2023-03-27

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. 1 key finding →

Evidence: low

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

Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics

✓ Peer-reviewed alzhub usefulness score 50 out of 100. Scientific reports 2019-02-19

In the Alzheimer's disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. 4 key findings →

Evidence: low

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

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase 18F-Florbetaben PET and clinical features

✓ Peer-reviewed alzhub usefulness score 50 out of 100. Scientific Reports 2025-07-01

This study developed machine learning models to predict Aβ positivity in Alzheimer’s disease by integrating early-phase 18 F-Florbetaben PET and clinical data to improve diagnostic accuracy. 2 key findings →

Participants: 176 Evidence: low

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

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images

✓ Peer-reviewed alzhub usefulness score 45 out of 100. Scientific reports 2018-04-09

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

Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data

✓ Peer-reviewed alzhub usefulness score 45 out of 100. Scientific reports 2020-05-29

To estimate dementia incidence rates using Australian administrative datasets and compare the characteristics of people identified with dementia across different datasets. 5 key findings →

Evidence: moderate

Full summary, score breakdown and related studies → Original source

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia

✓ Peer-reviewed alzhub usefulness score 44 out of 100. Scientific reports 2018-09-05

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). 5 key findings →

Evidence: low

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

Identification of UBE2N as a biomarker of Alzheimer’s disease by combining WGCNA with machine learning algorithms

✓ Peer-reviewed alzhub usefulness score 43 out of 100. Scientific Reports 2025-02-22

Alzheimer’s disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its onset. 4 key findings →

Evidence: low

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

Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression

✓ Peer-reviewed alzhub usefulness score 42 out of 100. Scientific Reports 2025-07-16

Recently, dementia research has primarily concentrated on using Magnetic Resonance Imaging (MRI) to develop learning models in processing and analyzing brain data. 2 key findings →

Participants: 450 Evidence: low

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

Beneficial effect of artificial intelligence care call on memory and depression in community dwelling individuals with dementia

✓ Peer-reviewed alzhub usefulness score 39 out of 100. Scientific Reports 2025-07-25

Traditional dementia-prevention programs have limited accessibility and lack continuous monitoring, making sustained cognitive and emotional support challenging. 4 key findings →

Evidence: low

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

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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 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 →

Newest studies, as a dated timeline →    Atom feed