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47/100

✓ Peer-reviewed alzhub usefulness score 47 out of 100. Getting a diagnosis

Topic: Risk, prevention and care in population studies

The ACoE's overall cognitive assessments were significantly reliable (ICC [intraclass correlation coefficient]=0.89; P<.001). Each cognitive domain's assessments were also significantly reliable, including attention (ICC=0.74; PFWE<.001), language (ICC=0.89; PFWE<.001), memory (ICC=0.91; PFWE<.001), fluency (ICC=0.74; PFWE<.001), and visuospatial function (ICC=0.78; PFWE<.001). The ACoE was also able to successfully diagnose patients similarly to both paper-based tests (area under the receiver operating characteristic curve=0.96; PFWE<.001). In this study, we evaluated if the ACoE could reliably phenotype cognitive symptoms relative to the assessments of established standard paper-based cognitive assessments. We found that the ACoE reliably phenotypes patient cognition, which can be used to screen patients. In the future, these cognitive phenotypes may be used to diagnose specific etiologies.

Key findings

The ACoE's overall cognitive assessments were significantly reliable (ICC [intraclass correlation coefficient]=0.89; P<.001).
Each cognitive domain's assessments were also significantly reliable, including attention (ICC=0.74; PFWE<.001), language (ICC=0.89; PFWE<.001), memory (ICC=0.91; PFWE<.001), fluency (ICC=0.74; PFWE<.001), and visuospatial function (ICC=0.78; PFWE<.001).
The ACoE was also able to successfully diagnose patients similarly to both paper-based tests (area under the receiver operating characteristic curve=0.96; PFWE<.001).
In this study, we evaluated if the ACoE could reliably phenotype cognitive symptoms relative to the assessments of established standard paper-based cognitive assessments.
We found that the ACoE reliably phenotypes patient cognition, which can be used to screen patients.

Why it matters

This randomized controlled trial found that a machine learning-based cognitive assessment was reliable across cognitive domains and diagnosed patients similarly to standard paper-based tests. It is a screening and phenotyping tool, so it does not treat, cure or prevent Alzheimer's disease.

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Study details

Participants35
Evidence strengthHigh

How this study scored 47/100

Published in a peer-reviewed journal. Scored zero for study design and effect reported because the record does not state it.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Journal of Medical Internet Research)
Journal's citation record	3 / 4	Journal of Medical Internet Research: journal h-index 286 (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	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	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	1 / 4	35 participants
Effect reported	0 / 3	No effect size reported — scores zero (not stated in the record — scored 0)
Studied in people	3 / 3	Studied in people (participant count stated)
Randomised / blinded	1 / 2	Randomised (as stated in the record)
Times cited	1 / 7	Cited 2 times (OpenAlex, 2026-10-10)
Citations per year	0 / 5	0.7 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 1.3× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	0 / 4	Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	1 / 4	Last-listed author Marcus Ng: h-index 16 (OpenAlex, whole career)
Institution's track record	3 / 3	Harvard University: institution h-index 3,235 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 5 institution(s) in 3 countries
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 40737611)
Free to read	2 / 2	Free to read
Total	47 / 100	—

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Read the original

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

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.

APA 7th

Howard, C., Johnson, A. M., Baratono, S. R., Faust, K., Peedicail, J. S., & Ng, M. (2025). Machine Learning–Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial. Journal of Medical Internet Research. https://doi.org/10.2196/67446

Vancouver

Howard C, Johnson AM, Baratono SR, Faust K, Peedicail JS, Ng M. Machine Learning–Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial. Journal of Medical Internet Research. 2025. doi:10.2196/67446. PMID: 40737611.

BibTeX

@article{howard2025-40737611,
  author  = {Howard, Calvin and Johnson, Amy M and Baratono, Sheena R. and Faust, Katharina and Peedicail, Joseph Samuel and Ng, Marcus},
  title   = {{Machine Learning–Based Cognitive Assessment With The Autonomous Cognitive Examination: Randomized Controlled Trial}},
  journal = {Journal of Medical Internet Research},
  year    = {2025},
  doi     = {10.2196/67446},
  pmid    = {40737611},
  url     = {https://doi.org/10.2196/67446}
}

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