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Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis
Lee GY, Kim J, Kim JH, Kim K, Seong JK.. Healthcare informatics research, 2014-01-31
Topic: Amyloid, tau and blood-test diagnosis
Objectives Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. Methods We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. Results With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). Conclusions In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.
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APA 7th
Lee, G. Y., Kim, J., Kim, J. H., Kim, K., & Seong, J. K. (2014). Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis. Healthcare informatics research. https://doi.org/10.4258/hir.2014.20.1.61
Vancouver
Lee GY, Kim J, Kim JH, Kim K, Seong JK. Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis. Healthcare informatics research. 2014. doi:10.4258/hir.2014.20.1.61. PMID: 24627820.
BibTeX
@article{lee2014-24627820,
author = {Lee, G. Y. and Kim, J. and Kim, J. H. and Kim, K. and Seong, J. K.},
title = {{Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis}},
journal = {Healthcare informatics research},
year = {2014},
doi = {10.4258/hir.2014.20.1.61},
pmid = {24627820},
url = {https://doi.org/10.4258/hir.2014.20.1.61}
}
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Key findings
- Our smartphone agent implements the mobile classification and shows various results.
- Results With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%).
Study details
- Effect size87.33%; 96.49%; 64.33%
- Evidence strengthLow
How this study scored 43/100
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 25 of the 58 points we could judge it on, which is 43 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Healthcare informatics research) |
| 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 | 1 / 5 | 2014 — within 20 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 24627820) |
| Free to read | 2 / 2 | Free to read |
| Points | 25 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 25 of the 58 points we could judge it on is 43 out of 100. |
| Total | 43 / 100 | — |
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