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48/100

✓ Peer-reviewed alzhub usefulness score 48 out of 100. Biomarkers

Topic: Brain imaging, connectivity and assessment

There were significant positive correlations between the predicted MMSE scores and ground truth scores in the Patient and Healthy groups (r = 0.66, p < 0.001). There were no significant differences between the predicted MMSE scores and ground truth scores in the Patient group (p > 0.05); however, in the Healthy group, the predicted MMSE scores were slightly, but significantly, lower than the ground truth scores (p < 0.05). In the Health examination group, the DNN model classified 94 subjects as normal (MMSE = 27-30), 67 subjects as having mild cognitive impairment (24-26), and four subjects as having dementia (≤ 23). In 37 subjects in the Health examination group, the predicted MMSE scores were slightly lower than the ground truth MMSE (p < 0.05). In contrast, in the subjects with neurological disorders, such as subarachnoid hemorrhage, the ground truth MMSE scores were lower than the predicted scores. The DNN model could predict cognitive function accurately. The predicted MMSE scores were significantly lower than the ground truth scores in the Healthy and Health examination groups, while there was no significant difference in the Patient group. We suggest that the difference between the predicted and ground truth MMSE scores was caused by changes in atherosclerosis with aging, and that applying the DNN model to younger subjects may predict future cognitive impairment after the onset of atherosclerosis.

Key findings

There were significant positive correlations between the predicted MMSE scores and ground truth scores in the Patient and Healthy groups (r = 0.66, p < 0.001).
There were no significant differences between the predicted MMSE scores and ground truth scores in the Patient group (p > 0.05); however, in the Healthy group, the predicted MMSE scores were slightly, but significantly, lower than the ground truth scores (p < 0.05).
In the Health examination group, the DNN model classified 94 subjects as normal (MMSE = 27-30), 67 subjects as having mild cognitive impairment (24-26), and four subjects as having dementia (≤ 23).
In 37 subjects in the Health examination group, the predicted MMSE scores were slightly lower than the ground truth MMSE (p < 0.05).
In contrast, in the subjects with neurological disorders, such as subarachnoid hemorrhage, the ground truth MMSE scores were lower than the predicted scores.

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Study details

Participants202
Evidence strengthLow

How this study scored 48/100

Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded 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, multi-centre collaboration and free to read, so those were left out: it earned 27 of the 56 points we could judge it on, which is 48 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (Frontiers in neurology)
Journal's citation record	— / 4	No journal record from OpenAlex — left out of this score
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	2 / 5	2020 — within 10 year(s) of the newest study here
Sample size	2 / 4	202 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	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
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 33381075)
Free to read	— / 2	Access not recorded — left out of this score
Points	27 / 56	Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 27 of the 56 points we could judge it on is 48 out of 100.
Total	48 / 100	—

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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

Sakatani, K., Oyama, K., & Hu, L. (2020). Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination. Frontiers in neurology. https://doi.org/10.3389/fneur.2020.588140

Vancouver

Sakatani K, Oyama K, Hu L. Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination. Frontiers in neurology. 2020. doi:10.3389/fneur.2020.588140. PMID: 33381075.

BibTeX

@article{sakatani2020-33381075,
  author  = {Sakatani, Kaoru and Oyama, Katsunori and Hu, Lizhen},
  title   = {{Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination}},
  journal = {Frontiers in neurology},
  year    = {2020},
  doi     = {10.3389/fneur.2020.588140},
  pmid    = {33381075},
  url     = {https://doi.org/10.3389/fneur.2020.588140}
}

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