Home › Research › Digital health tools
The potential role of machine learning and deep learning in differential diagnosis of Alzheimer’s disease and FTD using imaging biomarkers: A review
Sara Mirabian, Fatemeh Mohammadian, Zohreh Ganji, Hoda Zare, Erfan Hasanpour Khalesi. The Neuroradiology Journal, 2025-01-09
Topic: Amyloid, tau and blood-test diagnosis
IntroductionThe prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.Materials and MethodsThis narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models.ResultsAmong the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks.ConclusionThis review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Mirabian, S., Mohammadian, F., Ganji, Z., Zare, H., & Khalesi, E. H. (2025). The potential role of machine learning and deep learning in differential diagnosis of Alzheimer’s disease and FTD using imaging biomarkers: A review. The Neuroradiology Journal. https://doi.org/10.1177/19714009251313511
Vancouver
Mirabian S, Mohammadian F, Ganji Z, Zare H, Khalesi EH. The potential role of machine learning and deep learning in differential diagnosis of Alzheimer’s disease and FTD using imaging biomarkers: A review. The Neuroradiology Journal. 2025. doi:10.1177/19714009251313511. PMID: 39787363.
BibTeX
@article{mirabian2025-39787363,
author = {Mirabian, Sara and Mohammadian, Fatemeh and Ganji, Zohreh and Zare, Hoda and Khalesi, Erfan Hasanpour},
title = {{The potential role of machine learning and deep learning in differential diagnosis of Alzheimer’s disease and FTD using imaging biomarkers: A review}},
journal = {The Neuroradiology Journal},
year = {2025},
doi = {10.1177/19714009251313511},
pmid = {39787363},
url = {https://doi.org/10.1177/19714009251313511}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- IntroductionThe prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies.
- Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.Materials and MethodsThis narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD.
Why it matters
This narrative review of 31 studies examines AI methods using imaging to tell Alzheimer's disease apart from frontotemporal dementia. It is about improving diagnosis, not about treating, curing, or preventing Alzheimer's disease.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Study designnarrative review
- Evidence strengthLow
How this study scored 40/100
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 senior author's track record, so that was left out: it earned 38 of the 96 points we could judge it on, which is 40 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (The Neuroradiology Journal) |
| Journal's citation record | 0 / 4 | The Neuroradiology Journal: journal h-index 41 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | Design recorded as “narrative review”, which our rubric does not rank (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 | 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 | 3 / 7 | Cited 15 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 5.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 9.5× 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 | 2 / 4 | Cited 15 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Erfan Hasanpour Khalesi holds only 2 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 0 / 3 | Mashhad University of Medical Sciences: institution h-index 285 (OpenAlex) |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39787363) |
| Free to read | 2 / 2 | Free to read |
| Points | 38 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 38 of the 96 points we could judge it on is 40 out of 100. |
| Total | 40 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here. Showing 12 of 18.
Later studies that cite this one
1 study in the alzhub library lists this one in its references.
Related studies in Digital health tools
Also tagged “classification”
835 studies in the library carry the topic tag “classification”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 835 tagged studies.