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Research

30,180 Alzheimer's and dementia studies in plain English, each scored out of 100.

Find studies

Showing 1,826–1,850 of 1,917 studies in Brain scans and imaging (page 74 of 77) · 30,180 in the whole library · How the 0–100 score works

Download bibliography of the first 500 of these 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; 15,654 studies have no institution on record and are not counted here.

What “Brain scans and imaging” covers

Brain scans and imaging — MRI, PET and other scans: what they show, how early, and how reliably.

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.

Alzheimer Hastalığının Tespiti CNN Model Sınıflandırması

✓ Peer-reviewed alzhub usefulness score 29 out of 100. International Journal of Pure and Applied Sciences 2025-06-27

Bilgisayar destekli cihaz ve sistemlerin sağlık sektöründe giderek artan kullanımı, hastalıkların daha erken ve daha hızlı teşhis edilmesine olanak sağlamaktadır. Bilgisayar destekli sistem ve cihazlar özellikle nörolojik hastalıkların tanı ve görüntülemesinde kritik bir rol oynamaktadır. Merkezi sinir sistemi ve bilişsel fonksiyonların değerlendirilmesindeki gelişmeler, özellikle Alzheimer Hastalığı gibi nörodejeneratif hastalıkların erken teşhisinde önemli avantajlar sağlamaktadır. Çalışmada kullanılan veriler Kaggle platformunda açık kaynaklı olarak bulunan “Augmented Alzheimer MRI Dataset” isimli veri setinden alınmıştır. Bu veri setinde ikinci klasör kullanılmıştır. Toplamda 33.894 adet görüntü bulunmaktadır. Çalışmada Alzheimer hastalığı teşhisi analizi için derin öğrenme sınıflandırıcı model olarak Convolutional Neural Network (CNN) kullanılmıştır. Performans analizi yapılmıştır. Kullanılan CNN model accuracy değerini 0.9102 olarak vermiştir. 5 key findings →

Evidence: low

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

Performance evaluation of Alzheimer’s disease detection in brain MRI images using the Alzheimer-net classifier

✓ Peer-reviewed alzhub usefulness score 28 out of 100. Engineering Research Express 2025-06-26

The proposed approach achieves 97.9% AD Classification Index (ADCI) for the Kaggle dataset and 97.9% ADCI for the MIRIAD dataset. 3 key findings →

Evidence: low

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

Optimized Neural Network Architectures for Early Detection of Alzheimer\'s disease Using MRI Images

✓ Peer-reviewed alzhub usefulness score 28 out of 100. International Journal for Research in Applied Science and Engineering Technology 2025-04-30

Alzheimer's disease is a neurodegenerative disorder affecting millions globally, with early detection being critical for effective management. 2 key findings →

Evidence: low

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

Klasifikasi Penyakit Alzheimer menggunakan CNN dengan pretrained VGG19 dan SMOTE berdasarkan Citra MRI Otak

✓ Peer-reviewed alzhub usefulness score 28 out of 100. Arcitech Journal of Computer Science and Artificial Intelligence 2025-12-30

Early detection of Alzheimer's disease is crucial for effective treatment, and the use of brain MRI images has become a common method for diagnosis. 2 key findings →

Evidence: low

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

EARLY STAGES OF ALZHEIMERS DETECTION

✓ Peer-reviewed alzhub usefulness score 28 out of 100. Zenodo (CERN European Organization for Nuclear Research) 2025-11-20

The Early Stages of Alzheimer’s Detection project focuses on identifying signs of Alzheimer’s disease at the earliest possible stage using advanced machine learning and medical imaging techniques. 3 key findings →

Evidence: low

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

EARLY STAGES OF ALZHEIMERS DETECTION

✓ Peer-reviewed alzhub usefulness score 28 out of 100. Zenodo (CERN European Organization for Nuclear Research) 2025-11-20

The Early Stages of Alzheimer’s Detection project focuses on identifying signs of Alzheimer’s disease at the earliest possible stage using advanced machine learning and medical imaging techniques. 3 key findings →

Evidence: low

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

Alzheimers and Brain Tumor Detection Using Deep Learning

✓ Peer-reviewed alzhub usefulness score 28 out of 100. International Journal of Innovative Science and Research Technology 2025-05-02

The results may contribute to refining current diagnostic techniques, promoting earlier detection, and assisting in the development of advanced tools for accurate brain tumor diagnosis and treatment planning. 1 key finding →

Evidence: low

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

Application of Convolutional Neural Network Based on ResNet18 for Alzheimer Disease Classification

✓ Peer-reviewed alzhub usefulness score 28 out of 100. International Journal of Artificial Intelligence Research 2025-12-30

Alzheimer's disease is a form of progressive dementia that significantly impacts the quality of life of patients and their families. 3 key findings →

Evidence: low

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

Alzheimer Detection Using Deep Learning Methods

✓ Peer-reviewed alzhub usefulness score 27 out of 100. Journal of Intelligent Systems and Internet of Things 2025-01-01

This study proposes a deep learning-based framework to detect and classify Alzheimer's disease (AD) in the early stages using medical imaging, and specifically Magnetic Resonance Imaging (MRI). 1 key finding →

Evidence: low

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

Ophthalmic imaging and artificial intelligence in early diagnosis of Alzheimer disease

✓ Peer-reviewed alzhub usefulness score 27 out of 100. Neurology Bulletin 2025-12-16

Alzheimer disease represents a major medical and social challenge owning to the complexity of its diagnosis, especially at the preclinical stage. 1 key finding →

Evidence: low

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

ENHANCED ALZHEIMER DISEASE DIAGNOSIS THROUGH DEEP LEARNING-BASED MRI SEGMENTATION AND CLASSIFICATION

✓ Peer-reviewed alzhub usefulness score 26 out of 100. RCHUB JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE) 2026-07-01

The accurate diagnosis tends to remain critical for the improvement of the patient outcomes in the Alzheimer’s disease (AD), where a progressive neurodegenerative disorder with the growing of the global impact. 2 key findings →

Evidence: low

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

A survey on various Alzheimer classification techniques using 3D MRI images: a challenging overview

✓ Peer-reviewed alzhub usefulness score 25 out of 100. International Journal of Information and Decision Sciences 2025-01-01

This survey presents 50 research papers focussed on various techniques in Alzheimer classification techniques using 3D MRI images, and the categorisation of the techniques is made based on the fusion-based, convolutional neural network (CNN)-based, random forest (RF)-based and support vector machine (SVM)-based approaches. 1 key finding →

Evidence: low

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

RETRACTED: Revolutionizing medical imaging: A cutting-edge AI framework with vision transformers and perceiver IO for multi-disease diagnosis

⚠ Retracted — do not rely on it alzhub usefulness score 16 out of 100. Computational Biology and Chemistry 2025-07-04

The integration of artificial intelligence in medical image classification has significantly advanced disease detection. 2 key findings →

Evidence: low

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

Impaired cerebrovascular-cerebrospinal fluid coupling is associated with non-motor features of Lewy body disease

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv (preprint) 2026-08-19

Parkinsons disease and dementia with Lewy bodies patients both demonstrated weaker global BOLD-CSF coupling compared to controls, with no detectable difference between patient groups. 4 key findings →

Participants: 39 Evidence: low

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

Deep multimodal clustering identifies biological subtypes of normal aging with divergent cognitive decline risk

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv (preprint) 2026-08-19

Cognitively normal older adults are often regarded as a homogeneous population in preventive and disease-modifying clinical trials for dementia. 2 key findings →

Design: prospective cohort Participants: 1,136 Evidence: moderate

Full summary, score breakdown and related studies → Original source

Explainable Deep Learning Reveals Distributed Neurodegeneration Signatures of Neuropsychiatric Symptoms Across the Alzheimer's Continuum

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv (preprint) 2026-08-27

Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease progression in Alzheimers disease (AD), yet their relationship with neurodegeneration remain poorly characterized. 2 key findings →

Participants: 1,756 Evidence: low

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

Meta-analysis of glucose metabolism across Alzheimer's, Parkinson's and ALS reveals distinct patterns of cortical hypometabolism versus subcortical hypermetabolism with disease-specific spatial signatures

⚠ Preprint — not yet peer-reviewed alzhub usefulness score 16 out of 100. medRxiv (preprint) 2026-08-31

The purpose of this meta-analysis was to determine the common signature of dysregulated brain glucose metabolism in late-onset age-associated neurodegenerative diseases (A2NDs), including Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis. 2 key findings →

Design: meta-analysis Participants: 5,298 Evidence: high

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 30,180 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 30,180 records, built mostly from automated pulls of Europe PMC (64.2% of the library) and OpenAlex (16.9%), 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 (3.4% 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