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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence
Sahar Rezaei, Farzan Asadirad, Arash Motamed, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar et al.. NeuroImage, 2025-07-16
Topic: Amyloid, tau and blood-test diagnosis
This comprehensive review examines the integration of Quantitative Electroencephalography (qEEG) and Artificial Intelligence (AI) in the detection and diagnosis of Alzheimer's Disease (AD). Through systematic analysis of 11 key studies across multiple international databases, we evaluated various AI architectures, including machine learning algorithms and deep learning networks, applied to qEEG data for AD detection. The review encompasses studies with diverse subject populations, ranging from 35 to 890 participants, with mean ages between 66.94 and 74.8 years. Results demonstrate that AI-enhanced qEEG analysis achieves remarkable diagnostic accuracy, with Linear Discriminant Analysis (LDA) reaching 93.18% accuracy and 97.92% Area Under Curve (AUC). Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) also showed promising results, with some models achieving up to 100% sensitivity in specific classifications. The integration of multiple data types and advanced feature extraction methods significantly improved diagnostic precision. Geographic diversity in research origins, spanning from Asia to Europe and the Americas, provides robust cross-cultural validation of findings. However, challenges persist in data quality, computational resources, and standardization of methodologies. This review highlights the significant potential of AI-enhanced qEEG as a non-invasive, cost-effective tool for the diagnosis of AD in its prodromal and dementia stages, while also identifying areas requiring further research to optimize its clinical application. These findings suggest that AI-enhanced qEEG analysis could revolutionize AD diagnosis, enabling earlier intervention and improved patient outcomes.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Rezaei, S., Asadirad, F., Motamed, A., Kamran, M., Parsa, F., Samimi, H., et al. (2025). Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence. NeuroImage. https://doi.org/10.1016/j.neuroimage.2025.121373
Vancouver
Rezaei S, Asadirad F, Motamed A, Kamran M, Parsa F, Samimi H, et al. Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence. NeuroImage. 2025. doi:10.1016/j.neuroimage.2025.121373. PMID: 40675424.
BibTeX
@article{rezaei2025-40675424,
author = {Rezaei, Sahar and Asadirad, Farzan and Motamed, Arash and Kamran, Mohammadsadegh and Parsa, Farzaneh and Samimi, Haniyeh and Ghannadikhosh, Parna and Zahmatyar, Mahdi and others},
title = {{Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence}},
journal = {NeuroImage},
year = {2025},
doi = {10.1016/j.neuroimage.2025.121373},
pmid = {40675424},
url = {https://doi.org/10.1016/j.neuroimage.2025.121373}
}
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.
Key findings
- Results demonstrate that AI-enhanced qEEG analysis achieves remarkable diagnostic accuracy, with Linear Discriminant Analysis (LDA) reaching 93.18% accuracy and 97.92% Area Under Curve (AUC).
- Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) also showed promising results, with some models achieving up to 100% sensitivity in specific classifications.
- The integration of multiple data types and advanced feature extraction methods significantly improved diagnostic precision.
- This review highlights the significant potential of AI-enhanced qEEG as a non-invasive, cost-effective tool for the diagnosis of AD in its prodromal and dementia stages, while also identifying areas requiring further research to optimize its clinical application.
- These findings suggest that AI-enhanced qEEG analysis could revolutionize AD diagnosis, enabling earlier intervention and improved patient outcomes.
Why it matters
This review reports that combining qEEG with AI achieved high diagnostic accuracy for detecting Alzheimer's disease in the studies examined. It addresses detection and diagnosis only, and notes ongoing challenges in data quality, computing resources and standardisation; it does not report any treatment or cure.
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Study details
- Participants890
- Effect size93.18%; 97.92%
- Evidence strengthLow
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (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 | 890 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 | 2 / 7 | Cited 8 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 2.7 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 5.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 1 / 4 | Cited 8 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Hossein Arabi: h-index 47 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University Hospital of Geneva: institution h-index 635 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40675424) |
| Free to read | 2 / 2 | Free to read |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2025.121373
- PubMed: 40675424
- Original source →
- Free full text: NeuroImage →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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