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Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review
Agarwal D, Marques G, de la Torre-Díez I, Franco Martin MA, García Zapiraín B, Martín Rodríguez F.. Sensors (Basel, Switzerland), 2021-10-31
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.
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APA 7th
Agarwal, D., Marques, G., de la Torre-Díez, I., Franco Martin, M. A., García Zapiraín, B., & Martín Rodríguez, F. (2021). Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review. Sensors (Basel, Switzerland). https://doi.org/10.3390/s21217259
Vancouver
Agarwal D, Marques G, de la Torre-Díez I, Franco Martin MA, García Zapiraín B, Martín Rodríguez F. Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review. Sensors (Basel, Switzerland). 2021. doi:10.3390/s21217259. PMID: 34770565.
BibTeX
@article{agarwal2021-34770565,
author = {Agarwal, D. and Marques, G. and de la Torre-Díez, I. and Franco Martin, M. A. and García Zapiraín, B. and Martín Rodríguez, F.},
title = {{Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review}},
journal = {Sensors (Basel, Switzerland)},
year = {2021},
doi = {10.3390/s21217259},
pmid = {34770565},
url = {https://doi.org/10.3390/s21217259}
}
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Key findings
- Five databases were used and the results before screening report 215 studies published between 2010 and 2020.
- The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD.
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Study details
- Study designsystematic review
- Effect size98.20%; 87.78%
- Evidence strengthHigh
How this study scored 74/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size 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 43 of the 58 points we could judge it on, which is 74 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Sensors (Basel, Switzerland)) |
| 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 |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2021 — within 6 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 34770565) |
| Free to read | 2 / 2 | Free to read |
| Points | 43 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 43 of the 58 points we could judge it on is 74 out of 100. |
| Total | 74 / 100 | — |
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