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74/100

✓ Peer-reviewed alzhub usefulness score 74 out of 100. Long-term cohort studies

Topic: Amyloid, tau and blood-test diagnosis

Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly developing field of neuroimaging with strong potential to be used in practice. In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications. In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness.

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

APA 7th

Lebedev, A. V., Westman, E., Van Westen, G. J., Kramberger, M. G., Lundervold, A., Aarsland, D., et al. (2014). Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness. NeuroImage. Clinical. https://doi.org/10.1016/j.nicl.2014.08.023

Vancouver

Lebedev AV, Westman E, Van Westen GJ, Kramberger MG, Lundervold A, Aarsland D, et al. Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness. NeuroImage. Clinical. 2014. doi:10.1016/j.nicl.2014.08.023. PMID: 25379423.

BibTeX

@article{lebedev2014-25379423,
  author  = {Lebedev, A. V. and Westman, E. and Van Westen, G. J. and Kramberger, M. G. and Lundervold, A. and Aarsland, D. and Soininen, H. and Kłoszewska, I. and others},
  title   = {{Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness}},
  journal = {NeuroImage. Clinical},
  year    = {2014},
  doi     = {10.1016/j.nicl.2014.08.023},
  pmid    = {25379423},
  url     = {https://doi.org/10.1016/j.nicl.2014.08.023}
}

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Key findings

In this context, assessment of models' robustness to noise and imaging protocol differences together with post-processing and tuning strategies are key tasks to be addressed in order to move towards successful clinical applications.
In this study, we investigated the efficacy of Random Forest classifiers trained using different structural MRI measures, with and without neuroanatomical constraints in the detection and prediction of AD in terms of accuracy and between-cohort robustness.
From The ADNI database, 185 AD, and 225 healthy controls (HC) were randomly split into training and testing datasets. 165 subjects with mild cognitive impairment (MCI) were distributed according to the month of conversion to dementia (4-year follow-up).
Structural 1.5-T MRI-scans were processed using Freesurfer segmentation and cortical reconstruction.

Why it matters

This study tested Random Forest classifiers using structural MRI measures for detecting and predicting Alzheimer's disease, including how robust they are across cohorts. It is about computer-aided diagnosis, not about treating, curing or preventing the disease.

Study details

Participants165
Effect size88.6%; 92.0%; 79.5%
Evidence strengthModerate

How this study scored 74/100

Published in a peer-reviewed journal, and it is a randomized controlled trial.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (NeuroImage. Clinical)
Journal's citation record	1 / 4	NeuroImage Clinical: journal h-index 133 (OpenAlex, 2026-10-29)
Study design	11 / 12	A randomized controlled trial (from PubMed type “Randomized Controlled Trial”)
Strength of evidence	3 / 5	Rated “moderate” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	1 / 5	2014 — within 20 year(s) of the newest study here
Sample size	2 / 4	165 participants
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	3 / 3	Studied in people (participant count stated)
Randomized / blinded	1 / 2	Randomized (as stated in the record)
Times cited	6 / 7	Cited 307 times (OpenAlex, 2026-10-10)
Citations per year	4 / 5	21.9 citations a year over 14 year(s) since 2014
Impact for its field and year	4 / 6	Cited 9.2× 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	3 / 4	Cited 57 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	4 / 4	Last-listed author Andrew D. Simmons: h-index 102 (OpenAlex, whole career)
Institution's track record	2 / 3	Inserm: institution h-index 1,988 (OpenAlex)
Multicenter collaboration	3 / 3	OpenAlex lists 16 institution(s) in 9 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 25379423)
Free to read	2 / 2	Free to read
Total	74 / 100	—

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