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68/100

✓ Peer-reviewed alzhub usefulness score 68 out of 100. Brain scans and imaging

Topic: Amyloid, tau and blood-test diagnosis

Twelve articles-published between the 2007 and 2017-have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer's disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as, MRI morphometric, diffusion tensor imaging, and PET images. We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.

Key findings

Twelve articles-published between the 2007 and 2017-have been included in this systematic review after a quantitative and qualitative selection.
The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer's disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date.
Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as, MRI morphometric, diffusion tensor imaging, and PET images.
We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.

Why it matters

A review of twelve studies found that a machine-learning method called random forest, applied to combined imaging data, produced some of the best accuracy for predicting conversion from mild cognitive impairment to Alzheimer's disease. This is about improving prediction, not about treating or curing the disease.

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Study details

Study designsystematic review
Evidence strengthHigh

How this study scored 68/100

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.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in aging neuroscience)
Journal's citation record	2 / 4	Frontiers in Aging Neuroscience: journal h-index 174 (OpenAlex, 2026-10-29)
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	2 / 5	2017 — 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	6 / 7	Cited 647 times (OpenAlex, 2026-10-10)
Citations per year	5 / 5	58.8 citations a year over 11 year(s) since 2017
Impact for its field and year	3 / 6	Cited 3.9× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	3 / 6	Cited by 11 other studies in this library
Still being cited	4 / 4	Cited 262 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	3 / 4	Last-listed author Aldo Quattrone: h-index 78 (OpenAlex, whole career)
Institution's track record	1 / 3	National Research Council: institution h-index 840 (OpenAlex)
Multi-centre collaboration	1 / 3	OpenAlex lists 2 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 29056906)
Free to read	2 / 2	Free to read
Total	68 / 100	—

What each of these 20 components measures, and what this score is not →

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Cite this study APA, Vancouver, BibTeX · download .bib or .ris

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APA 7th

Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2017.00329

Vancouver

Sarica A, Cerasa A, Quattrone A. Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review. Frontiers in aging neuroscience. 2017. doi:10.3389/fnagi.2017.00329. PMID: 29056906.

BibTeX

@article{sarica2017-29056906,
  author  = {Sarica, A. and Cerasa, A. and Quattrone, A.},
  title   = {{Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review}},
  journal = {Frontiers in aging neuroscience},
  year    = {2017},
  doi     = {10.3389/fnagi.2017.00329},
  pmid    = {29056906},
  url     = {https://doi.org/10.3389/fnagi.2017.00329}
}

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