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57/100

✓ Peer-reviewed alzhub usefulness score 57 out of 100. Digital health tools

Topic: Amyloid, tau and blood-test diagnosis

Background Early and accurate diagnosis of Alzheimer's disease (AD) is essential for disease management and therapeutic choices that can delay disease progression. Machine learning (ML) approaches have been extensively used in attempts to develop algorithms for reliable early diagnosis of AD, although clinical usefulness, interpretability, and generalizability of the classifiers across datasets and MRI protocols remain limited. Methods We report a multi-diagnostic and generalizable approach for mild cognitive impairment (MCI) and AD diagnosis using structural MRI and ML. Classifiers were trained and tested using subjects from the AD Neuroimaging Initiative (ADNI) database (n = 570) and the Open Access Series of Imaging Studies (OASIS) project database (n = 531). Several classifiers are compared and combined using voting for a decision. Additionally, we report tests of generalizability across datasets and protocols (IR-SPGR and MPRAGE), the impact of using graph theory measures on diagnostic classification performance, the relative importance of different brain regions on classification for better interpretability, and an evaluation of the potential for clinical applicability of the classifier. Results Our "healthy controls (HC) vs. AD" classifier trained and tested on the combination of ADNI and OASIS datasets obtained a balanced accuracy (BAC) of 90.6% and a Matthew's correlation coefficient (MCC) of 0.811. Our "HC vs. MCI vs. AD" classifier trained and tested on the ADNI dataset obtained a 62.1% BAC (33.3% being the by-chance cut-off) and 0.438 MCC. Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression. Classifiers generalized well across both datasets and protocols. Finally, using graph theory measures did not improve classification performance. Conclusions In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance. Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.

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APA 7th

Diogo, V. S., Ferreira, H. A., Prata, D., & Alzheimer’s Disease Neuroimaging Initiative. (2022). Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-022-01047-y

Vancouver

Diogo VS, Ferreira HA, Prata D, Alzheimer’s Disease Neuroimaging Initiative. Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach. Alzheimer's research & therapy. 2022. doi:10.1186/s13195-022-01047-y. PMID: 35922851.

BibTeX

@article{diogo2022-35922851,
  author  = {Diogo, V. S. and Ferreira, H. A. and Prata, D. and {Alzheimer’s Disease Neuroimaging Initiative}},
  title   = {{Early diagnosis of Alzheimer's disease using machine learning: a multi-diagnostic, generalizable approach}},
  journal = {Alzheimer's research \& therapy},
  year    = {2022},
  doi     = {10.1186/s13195-022-01047-y},
  pmid    = {35922851},
  url     = {https://doi.org/10.1186/s13195-022-01047-y}
}

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Key findings

Hippocampal features were the strongest contributors to the classification decisions (approx. 25-45%), followed by temporal (approx. 13%), cingulate, and frontal regions (approx. 8-13% each), which is consistent with our current understanding of AD and its progression.
Classifiers generalized well across both datasets and protocols.
Finally, using graph theory measures did not improve classification performance.
Conclusions In sum, we present a diagnostic tool for MCI and AD trained using baseline scans and a follow-up diagnosis regardless of progression, which is multi-diagnostic, generalizable across independent data sources and acquisition protocols, and with transparently reported performance.
Rated as potentially clinically applicable, our tool may be clinically useful to inform diagnostic decisions in dementia, if successful in real-world prospective clinical trials.

Why it matters

Machine learning classifiers using structural MRI achieved 90.6% balanced accuracy in distinguishing Alzheimer's disease from healthy controls, with hippocampal features as the strongest contributors. This is a diagnostic tool and does not treat, cure, or prevent Alzheimer's disease.

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Study details

Participants570
Effect size90.6%; 62.1%; 33.3%
Evidence strengthLow

How this study scored 57/100

Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 55 of the 96 points we could judge it on, which is 57 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Alzheimer's research & therapy)
Journal's citation record	1 / 4	Alzheimer s Research & Therapy: journal h-index 146 (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	3 / 5	2022 — within 6 year(s) of the newest study here
Sample size	2 / 4	570 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)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	5 / 7	Cited 186 times (OpenAlex, 2026-10-10)
Citations per year	4 / 5	31.0 citations a year over 6 year(s) since 2022
Impact for its field and year	5 / 6	Cited 21.2× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	2 / 6	Cited by 6 other studies in this library
Still being cited	3 / 4	Cited 160 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	— / 4	Senior author's record not available — left out of this score
Institution's track record	1 / 3	King's College London: institution h-index 1,348 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 4 institution(s) in 2 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 35922851)
Free to read	2 / 2	Free to read
Points	55 / 96	Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 55 of the 96 points we could judge it on is 57 out of 100.
Total	57 / 100	—

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