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A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, Vitali P, Anzalone N et al.. Frontiers in neuroinformatics, 2020-06-11
Topic: Amyloid, tau and blood-test diagnosis
Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a "mixed VD-AD dementia" (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.
Key findings
- ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up.
- Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles.
Why it matters
Using MRI features from 60 subjects, this study found that machine learning algorithms had high discriminant power to classify Alzheimer disease versus vascular dementia, and the approach was then used to predict disease prevalence in 15 patients with a mixed profile. This is a diagnostic classification study and does not test any treatment, cure or prevention.
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Study details
- Participants60
- Effect size77.33%
- 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 (Frontiers in neuroinformatics) |
| Journal's citation record | 1 / 4 | Frontiers in Neuroinformatics: journal h-index 111 (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 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 60 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 133 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 16.6 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 4 / 6 | Cited 11.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 3 / 6 | Cited by 10 other studies in this library |
| Still being cited | 3 / 4 | Cited 60 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Claudia A. M. Gandini Wheeler‐Kingshott: h-index 76 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University College London: institution h-index 2,029 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 7 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32595465) |
| Free to read | 2 / 2 | Free to read |
| Total | 56 / 100 | — |
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Read the original
- DOI: 10.3389/fninf.2020.00025
- PubMed: 32595465
- Original source →
- Free full text: Frontiers in Neuroinformatics →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Castellazzi, G., Cuzzoni, M. G., Cotta Ramusino, M., Martinelli, D., Denaro, F., Ricciardi, A., et al. (2020). A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Frontiers in neuroinformatics. https://doi.org/10.3389/fninf.2020.00025
Vancouver
Castellazzi G, Cuzzoni MG, Cotta Ramusino M, Martinelli D, Denaro F, Ricciardi A, et al. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Frontiers in neuroinformatics. 2020. doi:10.3389/fninf.2020.00025. PMID: 32595465.
BibTeX
@article{castellazzi2020-32595465,
author = {Castellazzi, G. and Cuzzoni, M. G. and Cotta Ramusino, M. and Martinelli, D. and Denaro, F. and Ricciardi, A. and Vitali, P. and Anzalone, N. and others},
title = {{A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features}},
journal = {Frontiers in neuroinformatics},
year = {2020},
doi = {10.3389/fninf.2020.00025},
pmid = {32595465},
url = {https://doi.org/10.3389/fninf.2020.00025}
}
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