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Leveraging Artificial Intelligence in the Diagnosis of Neurodegenerative Disorders (Alzheimer's and Dementia) and in Managing Medication Schedules"
Amr F. Shawish, shahed Mohamed, Aya Essam, Samia Abdel Aziz Ahmed, Shimaa Ahmed, Riham Mohamed, Mahmoud Maher, Nourhan Kamal et al.. Damanhour Journal of Intelligent Systems and Informatics, 2025-06-01
Topic: Amyloid, tau and blood-test diagnosis
The Alzheimer's disease (AD) is recognized as one of the most common neurodegenerative causes of dementia worldwide, having been characterized by progressive cognitive decline for the most part. Early diagnosis is crucial for effective management and intervention. It has been widely recognized recently that deep learning (DL) has been enlightened as a powerful tool to automate the analysis of medical images, especially when it comes to magnetic resonance imaging (MRI), to detect and classify the stages of AD. Consequently, AI-Based Alzheimer’s Diagnostic and Assistance System (AIDAS) leverages artificial intelligence (AI) and deep learning technologies to facilitate the automated identification, classification, and monitoring of AD progression through sophisticated analysis of neuroimaging data. Furthermore, AIDAS integrates intelligent assistance features such as personalized medication reminders and cognitive support tools, thereby enhancing patient adherence, optimizing clinical decision-making, and contributing to a more proactive and precision-driven approach to neurodegenerative disease management. The investigation employed a dataset consisting of 80,000 MRI brain scans acquired along the brain's z-axis, with class labels derived from the Clinical Dementia Rating (CDR) guidelines. This dataset includes 461 people and was derived from the Open Access Series of Imaging Study (OASIS). The experimental results underscore that the proposed ordinal EfficientNetB0 architecture outperforms other models in fundamental structural tasks, characterized by minimal computational overhead, reduced susceptibility to overfitting, efficient memory utilization, and effective temporal regulation. Furthermore, the developed Convolutional Neural Network (CNN) model demonstrated exceptional performance, achieving classification accuracies of 99.5% for AD stage identification.
Key findings
- The experimental results underscore that the proposed ordinal EfficientNetB0 architecture outperforms other models in fundamental structural tasks, characterized by minimal computational overhead, reduced susceptibility to overfitting, efficient memory utilization, and effective temporal regulation.
- Furthermore, the developed Convolutional Neural Network (CNN) model demonstrated exceptional performance, achieving classification accuracies of 99.5% for AD stage identification.
Why it matters
This work describes an AI-based system using MRI scans to identify and classify Alzheimer's disease stages, reporting high classification accuracy, and also includes features such as medication reminders and cognitive support tools. It is about diagnosis and assistance, not about treating, curing or preventing the disease.
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Study details
- Participants461
- Effect size99.5%
- Evidence strengthLow
How this study scored 35/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 34 of the 96 points we could judge it on, which is 35 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Damanhour Journal of Intelligent Systems and Informatics) |
| Journal's citation record | 0 / 4 | Damanhour Journal of Intelligent Systems and Informatics: journal h-index 2 (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 | 2 / 4 | 461 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 1 / 6 | Cited 0.6× 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 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Hoda El-Batrawy holds only 9 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Damanhour University: institution h-index 126 (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 | 34 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 34 of the 96 points we could judge it on is 35 out of 100. |
| Total | 35 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.21608/djis.2025.389085.1008
- Original source →
- Free full text: Damanhour Journal of Intelligent Systems and Informatics →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Shawish, A. F., Mohamed, s., Essam, A., Ahmed, S. A. A., Ahmed, S., Mohamed, R., et al. (2025). Leveraging Artificial Intelligence in the Diagnosis of Neurodegenerative Disorders (Alzheimer's and Dementia) and in Managing Medication Schedules". Damanhour Journal of Intelligent Systems and Informatics. https://doi.org/10.21608/djis.2025.389085.1008
Vancouver
Shawish AF, Mohamed s, Essam A, Ahmed SAA, Ahmed S, Mohamed R, et al. Leveraging Artificial Intelligence in the Diagnosis of Neurodegenerative Disorders (Alzheimer's and Dementia) and in Managing Medication Schedules". Damanhour Journal of Intelligent Systems and Informatics. 2025. doi:10.21608/djis.2025.389085.1008.
BibTeX
@article{shawish2025-c8e26fbf,
author = {Shawish, Amr F. and Mohamed, shahed and Essam, Aya and Ahmed, Samia Abdel Aziz and Ahmed, Shimaa and Mohamed, Riham and Maher, Mahmoud and Kamal, Nourhan and others},
title = {{Leveraging Artificial Intelligence in the Diagnosis of Neurodegenerative Disorders (Alzheimer's and Dementia) and in Managing Medication Schedules"}},
journal = {Damanhour Journal of Intelligent Systems and Informatics},
year = {2025},
doi = {10.21608/djis.2025.389085.1008},
url = {https://doi.org/10.21608/djis.2025.389085.1008}
}
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