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Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Ebrahimighahnavieh MA, Luo S, Chiong R.. Computer methods and programs in biomedicine, 2019-11-27
Topic: Brain imaging, connectivity and assessment
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.
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APA 7th
Ebrahimighahnavieh, M. A., Luo, S., & Chiong, R. (2020). Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Computer methods and programs in biomedicine. https://doi.org/10.1016/j.cmpb.2019.105242
Vancouver
Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Computer methods and programs in biomedicine. 2020. doi:10.1016/j.cmpb.2019.105242. PMID: 31837630.
BibTeX
@article{ebrahimighahnavieh2020-31837630,
author = {Ebrahimighahnavieh, M. A. and Luo, S. and Chiong, R.},
title = {{Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review}},
journal = {Computer methods and programs in biomedicine},
year = {2020},
doi = {10.1016/j.cmpb.2019.105242},
pmid = {31837630},
url = {https://doi.org/10.1016/j.cmpb.2019.105242}
}
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Key findings
- From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available.
- Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques.
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Study details
- Evidence strengthLow
How this study scored 59/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 34 of the 58 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Computer methods and programs in biomedicine) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 12 / 12 | A systematic review of all the studies on the question (from PubMed type “Systematic Review”) |
| 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 | 2020 — 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 | A review of other studies — randomisation is not credited here |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31837630) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 34 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 34 of the 58 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
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