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28/100

✓ Peer-reviewed alzhub usefulness score 28 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Alzheimer's disease is a progressive neurodegenerative disorder that significantly impacts memory, cognition, and daily functioning. Early diagnosis plays a crucial role in slowing disease progression and improving patient care. This paper presents NeuroCapsule, an intelligent system designed for early detection of Alzheimer's disease using brain MRI images combined with artificial intelligence techniques. The proposed approach employs convolutional neural networks (CNNs) for automated feature extraction and classification of MRI scans into multiple cognitive stages, including Non-Demented, Very Mild Dementia, Mild Dementia, and Moderate Dementia. To address class imbalance and computational constraints, balanced sampling strategies and image downsampling techniques are applied. The system is evaluated on the OASIS MRI dataset, demonstrating high classification accuracy and efficient performance across both binary and multiclass tasks. Unlike traditional convolutional neural networks, NeuroCapsule extends beyond prediction by integrating a secure Digital Memory Vault that enables users to store reminders, notes, and personal records, supporting continuous cognitive management. Experimental results indicate that the proposed system achieves a favorable trade-off between accuracy and computational efficiency, making it suitable for real-world deployment. The integration of predictive analytics with memory assistance enhances both clinical relevance and patient-centered care, positioning NeuroCapsule as a comprehensive solution for Alzheimer's disease detection and management.

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

APA 7th

Gharve, R., Surwade, P., Ambre, S., Shirsath, P., & Pingale, S. B. (2026). Neurocapsule : Alzheimer Risk Predictor and Digital Memory Vault. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.19451714

Vancouver

Gharve R, Surwade P, Ambre S, Shirsath P, Pingale SB. Neurocapsule : Alzheimer Risk Predictor and Digital Memory Vault. Zenodo (CERN European Organization for Nuclear Research). 2026. doi:10.5281/zenodo.19451714.

BibTeX

@article{gharve2026-6f9a11db,
  author  = {Gharve, Rucha and Surwade, Pratik and Ambre, Shejal and Shirsath, Pareenita and Pingale, Suresh B.},
  title   = {{Neurocapsule : Alzheimer Risk Predictor and Digital Memory Vault}},
  journal = {Zenodo (CERN European Organization for Nuclear Research)},
  year    = {2026},
  doi     = {10.5281/zenodo.19451714},
  url     = {https://doi.org/10.5281/zenodo.19451714}
}

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Key findings

Alzheimer's disease is a progressive neurodegenerative disorder that significantly impacts memory, cognition, and daily functioning.
Experimental results indicate that the proposed system achieves a favorable trade-off between accuracy and computational efficiency, making it suitable for real-world deployment.

Why it matters

This paper presents NeuroCapsule, a system using convolutional neural networks on MRI scans to classify cognitive stages, plus a Digital Memory Vault for storing reminders and personal records. It reports a favorable trade-off between accuracy and computational efficiency on the OASIS dataset, but it is a detection and management tool, not a treatment, cure or prevention.

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Study details

Evidence strengthLow

How this study scored 28/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record and senior author's track record, so those were left out: it earned 26 of the 92 points we could judge it on, which is 28 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Zenodo (CERN European Organization for Nuclear Research))
Journal's citation record	— / 4	No journal record from OpenAlex — left out of this score
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	5 / 5	2026 — within 1 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	0 / 7	Cited 0 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.0 citations a year over 2 year(s) since 2026
Impact for its field and year	0 / 6	Cited 0.0× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	OpenAlex's profile for last-listed author Suresh B. Pingale holds only 3 work(s), too few to judge a track record — left out of this score
Institution's track record	0 / 3	University of Mumbai: institution h-index 220 (OpenAlex)
Multi-centre collaboration	0 / 3	OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete)
Indexed in PubMed	1 / 2	Not in PubMed, but has a DOI
Free to read	2 / 2	Free to read
Points	26 / 92	Scored on 18 of 20 parts. We have no data for the rest, so they are left out: 26 of the 92 points we could judge it on is 28 out of 100.
Total	28 / 100	—

What each of these 20 components measures, and what this score is not →

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