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Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects
Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao H.. IEEE reviews in biomedical engineering, 2018-12-11
Topic: Risk, prevention and care in population studies
Dementia, a chronic and progressive cognitive declination of brain function caused by disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. The success of these approaches is mostly attributed to the application of machine learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the recent years. Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge. Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia. The main contributions of this review paper are as follows. 1) Based on the detailed analysis of the existing literature, this paper discusses neuroimaging procedures for dementia diagnosis. 2) It systematically explains the most recent machine learning techniques and, in particular, deep learning approaches for early detection of dementia.
Key findings
- Based on the rigorous review of the existing works, we have found that, while most of the studies focused on Alzheimer's disease, recent research has demonstrated reasonable performance in the identification of other types of dementia remains a major challenge.
- Multimodal imaging analysis deep learning approaches have shown promising results in the diagnosis of these other types of dementia.
Why it matters
This review found that most automated diagnostic studies using neuroimaging and machine learning have focused on Alzheimer's disease, while identifying other types of dementia remains a major challenge. Multimodal imaging with deep learning showed promising results for diagnosing these other dementias, but this is a review of existing work rather than a new treatment or cure.
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Study details
- Evidence strengthLow
How this study scored 44/100
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.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (IEEE reviews in biomedical engineering) |
| Journal's citation record | 1 / 4 | IEEE Reviews in Biomedical Engineering: journal h-index 101 (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 | 2 / 5 | 2019 — within 10 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 | 5 / 7 | Cited 145 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.1 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 7.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 51 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Hongen Liao: h-index 44 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Imperial College London: institution h-index 1,744 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30561351) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 44 / 100 | — |
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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. Check the citation against the original before you use it.
APA 7th
Ahmed, M. R., Zhang, Y., Feng, Z., Lo, B., Inan, O. T., & Liao, H. (2019). Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects. IEEE reviews in biomedical engineering. https://doi.org/10.1109/rbme.2018.2886237
Vancouver
Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao H. Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects. IEEE reviews in biomedical engineering. 2019. doi:10.1109/rbme.2018.2886237. PMID: 30561351.
BibTeX
@article{ahmed2019-30561351,
author = {Ahmed, M. R. and Zhang, Y. and Feng, Z. and Lo, B. and Inan, O. T. and Liao, H.},
title = {{Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects}},
journal = {IEEE reviews in biomedical engineering},
year = {2019},
doi = {10.1109/rbme.2018.2886237},
pmid = {30561351},
url = {https://doi.org/10.1109/rbme.2018.2886237}
}
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