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Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease
Kim JP, Kim J, Park YH, Park SB, Lee JS, Yoo S, Kim EJ, Kim HJ et al.. NeuroImage. Clinical, 2019-04-03
Topic: Amyloid, tau and blood-test diagnosis
Background In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patterns of atrophy among three FTD clinical syndromes including behavioral variant FTD (bvFTD), non-fluent/agrammatic variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA) give rise to the need for classification models at the individual level. In this study, we aimed to classify each individual subject into one of the diagnostic categories in a hierarchical manner by employing a machine learning-based classification method. Methods We recruited 143 patients with FTD, 50 patients with Alzheimer's disease (AD) dementia, and 146 cognitively normal subjects. All subjects underwent a three-dimensional volumetric brain magnetic resonance imaging (MRI) scan, and cortical thickness was measured using FreeSurfer. We applied the Laplace Beltrami operator to reduce noise in the cortical thickness data and to reduce the dimension of the feature vector. Classifiers were constructed by applying both principal component analysis and linear discriminant analysis to the cortical thickness data. For the hierarchical classification, we trained four classifiers using different pairs of groups: Step 1 - CN vs. FTD + AD, Step 2 - FTD vs. AD, Step 3 - bvFTD vs. PPA, Step 4 - svPPA vs. nfvPPA. To evaluate the classification performance for each step, we used a10-fold cross-validation approach, performed 1000 times for reliability. Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%). The classification accuracies of steps 1-4 were 86.1%, 90.8%, 86.9%, and 92.1%, respectively. Changes in the right frontotemporal area were critical for discriminating behavioral variant FTD from PPA. The left frontal lobe discriminated nfvPPA from svPPA, while the bilateral anterior temporal regions were critical for identifying svPPA. Conclusions In the present study, our automated classifier successfully classified FTD clinical subtypes with good to excellent accuracy. Our classifier may help clinicians diagnose FTD subtypes with subtle cortical atrophy and facilitate appropriate specific interventions.
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APA 7th
Kim, J. P., Kim, J., Park, Y. H., Park, S. B., Lee, J. S., Yoo, S., et al. (2019). Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease. NeuroImage. Clinical. https://doi.org/10.1016/j.nicl.2019.101811
Vancouver
Kim JP, Kim J, Park YH, Park SB, Lee JS, Yoo S, et al. Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease. NeuroImage. Clinical. 2019. doi:10.1016/j.nicl.2019.101811. PMID: 30981204.
BibTeX
@article{kim2019-30981204,
author = {Kim, J. P. and Kim, J. and Park, Y. H. and Park, S. B. and Lee, J. S. and Yoo, S. and Kim, E. J. and Kim, H. J. and others},
title = {{Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease}},
journal = {NeuroImage. Clinical},
year = {2019},
doi = {10.1016/j.nicl.2019.101811},
pmid = {30981204},
url = {https://doi.org/10.1016/j.nicl.2019.101811}
}
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Key findings
- Results The classification accuracy of the entire hierarchical classification tree was 75.8%, which was higher than that of the non-hierarchical classifier (73.0%).
Why it matters
This study used machine learning on brain MRI cortical thickness data to classify individuals into frontotemporal dementia, Alzheimer's disease or cognitively normal categories, with the hierarchical tree achieving 75.8% accuracy. It is a classification study and does not test any treatment, cure or prevention.
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Study details
- Participants143
- Effect size75.8%; 73.0%; 86.1%
- Evidence strengthLow
How this study scored 56/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage. Clinical) |
| Journal's citation record | 1 / 4 | NeuroImage Clinical: journal h-index 133 (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 | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 143 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 102 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 11.3 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 4 / 6 | Cited 8.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 11 other studies in this library |
| Still being cited | 2 / 4 | Cited 38 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Joon‐Kyung Seong: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 10 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30981204) |
| Free to read | 2 / 2 | Free to read |
| Total | 56 / 100 | — |
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Read the original
- DOI: 10.1016/j.nicl.2019.101811
- PubMed: 30981204
- Original source →
- Free full text: NeuroImage Clinical →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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