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This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

✓ Peer-reviewed alzhub usefulness score 45 out of 100. Digital health tools

Topic: Brain imaging, connectivity and assessment

Alzheimer's Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1-3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature.

Key findings

Study details

How this study scored 45/100
How this study's score is builtJournal tier 7 of 10; Journal's citation record left out, no data; Study design 0 of 12; Strength of evidence 1 of 5; Peer-review validation 10 of 10; How recent 2 of 5; Sample size 0 of 4; Effect reported 2 of 3; Studied in people 0 of 3; Randomised / blinded 0 of 2; Times cited left out, no data; Citations per year left out, no data; Impact for its field and year left out, no data; Cited by studies here left out, no data; Still being cited left out, no data; Senior author's track record left out, no data; Institution's track record left out, no data; Multi-centre collaboration left out, no data; Indexed in PubMed 2 of 2; Free to read 2 of 2. 26 of the 58 points it could be judged on, which is 45 of 100. Total 45 of 100.26 of the 58 points we could judge = 45 of 100

Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people 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 and multi-centre collaboration, so those were left out: it earned 26 of the 58 points we could judge it on, which is 45 out of 100.

ComponentPointsWhy
Journal tier 7 / 10 Tier 3 — peer-reviewed journal (Scientific reports)
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 2018 — 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 2 / 3 Effect reported as a number, without an interval or test
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 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 29632364)
Free to read 2 / 2 Free to read
Points 26 / 58 Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100.
Total 45 / 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

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

Lu, D., Popuri, K., Ding, G. W., Balachandar, R., Beg, M. F., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Scientific reports. https://doi.org/10.1038/s41598-018-22871-z

Vancouver

Lu D, Popuri K, Ding GW, Balachandar R, Beg MF, Alzheimer’s Disease Neuroimaging Initiative. Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. Scientific reports. 2018. doi:10.1038/s41598-018-22871-z. PMID: 29632364.

BibTeX

@article{lu2018-29632364,
  author  = {Lu, D. and Popuri, K. and Ding, G. W. and Balachandar, R. and Beg, M. F. and {Alzheimer’s Disease Neuroimaging Initiative}},
  title   = {{Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images}},
  journal = {Scientific reports},
  year    = {2018},
  doi     = {10.1038/s41598-018-22871-z},
  pmid    = {29632364},
  url     = {https://doi.org/10.1038/s41598-018-22871-z}
}

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