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40/100

✓ Peer-reviewed alzhub usefulness score 40 out of 100. Getting a diagnosis

Topic: Risk, prevention and care in population studies

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate care. As the number of dementia cases continues to rise, delivering optimal care becomes more complex. Machine learning (ML) plays a crucial role in addressing this challenge by utilizing medical data to enhance care planning and management for individuals at risk of various types of dementia. Magnetic resonance imaging (MRI) is a commonly used method for analyzing neurological disorders. Recent evidence highlights the benefits of integrating artificial intelligence (AI) techniques with MRI, significantly enhancing the diagnostic accuracy for different forms of dementia. This paper explores the use of AI in the automated detection and classification of dementia, aiming to streamline early diagnosis and improve patient outcomes. Integrating ML models into clinical practice can transform dementia care by enabling early detection, personalized treatment plans, and more effectual monitoring of disease progression. In this study, an Enhancing Automated Detection and Classification of Dementia in Thinking Inability Persons using Artificial Intelligence Techniques (EADCD-TIPAIT) technique is presented. The goal of the EADCD-TIPAIT technique is for the detection and classification of dementia in individuals with cognitive impairment using MRI imaging. The EADCD-TIPAIT method performs preprocessing to scale the input data using z-score normalization to obtain this. Next, the EADCD-TIPAIT technique performs a binary greylag goose optimization (BGGO)-based feature selection approach to efficiently identify relevant features that distinguish between normal and dementia-affected brain regions. In addition, the wavelet neural network (WNN) classifier is employed to detect and classify dementia. Finally, the improved salp swarm algorithm (ISSA) is implemented to choose the WNN technique's hyperparameters optimally. The stimulation of the EADCD-TIPAIT technique is examined under a Dementia prediction dataset. The performance validation of the EADCD-TIPAIT approach portrayed a superior accuracy value of 95.00% under diverse measures.

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

APA 7th

Alotaibi, S. D., & Alharbi, A. A. K. (2025). Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques. Scientific Reports. https://doi.org/10.1038/s41598-025-09124-6

Vancouver

Alotaibi SD, Alharbi AAK. Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques. Scientific Reports. 2025. doi:10.1038/s41598-025-09124-6. PMID: 40634463.

BibTeX

@article{alotaibi2025-40634463,
  author  = {Alotaibi, Shoayee Dlaim and Alharbi, Abeer A. K.},
  title   = {{Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques}},
  journal = {Scientific Reports},
  year    = {2025},
  doi     = {10.1038/s41598-025-09124-6},
  pmid    = {40634463},
  url     = {https://doi.org/10.1038/s41598-025-09124-6}
}

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Key findings

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate care.
Recent evidence highlights the benefits of integrating artificial intelligence (AI) techniques with MRI, significantly enhancing the diagnostic accuracy for different forms of dementia.

Why it matters

This paper presents an artificial intelligence technique for automated detection and classification of dementia using MRI. It is about diagnosis, not treatment, cure, or prevention.

New to this subject? Start with our plain-English guide: Getting a diagnosis →

Study details

Effect size95.00%
Evidence strengthLow

How this study scored 40/100

Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Scientific Reports)
Journal's citation record	3 / 4	Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29)
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	4 / 5	2025 — within 3 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	2 / 3	Effect reported as a number, without an interval or test
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	2 / 7	Cited 6 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	2.0 citations a year over 3 year(s) since 2025
Impact for its field and year	3 / 6	Cited 3.8× 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	1 / 4	Cited 6 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author Abeer A. K. Alharbi: h-index 6 (OpenAlex, whole career)
Institution's track record	0 / 3	University of Ha'il: institution h-index 184 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 3 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40634463)
Free to read	2 / 2	Free to read
Total	40 / 100	—

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

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