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Find studies

Showing 51–75 of 165 studies in Digital health tools · tagged Diagnosis (page 3 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.

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.

Isolated rapid eye movement sleep behavior disorder (iRBD) is a major prodromal marker of α-synucleinopathies, often preceding the clinical onset of Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy. 3 key findings →

Participants: 31 Evidence: moderate

Two patients with nonfluent progressive aphasia, who have been studied longitudinally, are contrasted with a group of 5 patients with fluent progressive aphasia or semantic dementia. 5 key findings →

Evidence: low

The SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. 4 key findings →

Participants: 115 Evidence: low

On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments. 1 key finding →

Evidence: low

The study of Alzheimer's disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. 5 key findings →

Evidence: low

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. 1 key finding →

Participants: 200 Evidence: moderate

Early identification of individuals at increased risk of mild cognitive impairment (MCI), a pre-dementia state, can provide a critical window for prompt intervention to prevent or reverse disease progression. 5 key findings →

Design: longitudinal study Participants: 87 Evidence: moderate

This study provides pathological validation that the new criteria assist with separating PPA cases with AD pathology into the new lv-PPA syndrome and found that a number of diagnostic clinical features (disinhibition, food preferences and naming) did not assist in discriminating the different FTD syndromes. 2 key findings →

Evidence: low

Unusual clinical presentations of cortical-basal ganglionic degeneration

✓ Peer-reviewed alzhub usefulness score 48 out of 100. Annals of neurology 1996-12-01

Cortical-basal ganglionic degeneration classically presents predominantly as a motor disorder with a unique constellation of histological alterations characterized by the presence of neuronal loss and gliosis in a selective distribution, swollen achromasic neurons, and extensive deposition of abnormal tau in neurons and glia. 2 key findings →

Evidence: low

Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer's disease (AD) in the elderly people. 3 key findings →

Participants: 23 Evidence: low

A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18 F-FDG PET of the Brain

✓ Peer-reviewed alzhub usefulness score 48 out of 100. Radiology 2018-11-06

By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. 3 key findings →

Participants: 1,002 Evidence: low

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

Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs. 5 key findings →

Evidence: low

Phonological and articulatory impairment in Alzheimer's disease: a case series

✓ Peer-reviewed alzhub usefulness score 47 out of 100. Brain and language 2000-11-01

We demonstrate that phonological and articulatory impairments may occur at presentation or early in the course of Alzheimer's disease, contrary to claims that these aspects of language production are relatively preserved until the final stages of this disease. 1 key finding →

Evidence: low

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

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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.

1,042 records (3.4% of the library) are not peer-reviewed — 967 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