A prospective multicenter validation study of a machine learning algorithm classifier on quantitative electroencephalogram for differentiating between dementia with lewy bodies and Alzheimer’s dementia
Open accessAbstract
Background and purpose: An early and accurate diagnosis of Dementia with Lewy bodies (DLB) is critical because treatments and prognosis of DLB are different from Alzheimer’s disease (AD). This study was carried out in Japan to validate an Electroencephalography (EEG)-derived machine learning algorithm for discriminating DLB from AD which developed based on a database of EEG records from two different European countries. Methods: In a prospective multicenter study, patients with probable DLB or with probable AD were enrolled in a 1:1 ratio. A continuous EEG segment of 150 seconds was recorded, and the EEG data was processed using MC-004, the EEG-based machine learning algorithm, with all clinical information blinded except for age and gender. Results: Eighteen patients with probable DLB and 21 patients with probable AD were the included for the analysis. The performance of MC-004 differentiating probable DLB from probable AD was 72.2% (95% CI 46.5–90.3%) for sensitivity, 85.7% (63.7–97.0%) for specificity, and 79.5% (63.5–90.7%) for accuracy. When limiting to subjects taking ≤5 mg donepezil, the sensitivity was 83.3% (95% CI 51.6–97.9), the specificity 89.5% (66.9–98.7), and the accuracy 87.1% (70.2–96.4). Conclusions: MC-004, the EEG-based machine learning algorithm, was able to discriminate between DLB and AD with fairly high accuracy. MC-004 is a promising biomarker for DLB, and has the potential to improve the detection of DLB in a diagnostic process.
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MC-004 achieved 72.2% sensitivity, 85.7% specificity, and 79.5% accuracy for discriminating DLB from AD in a multicenter validation study of 18 probable DLB patients and 21 probable AD patients.
“MC-004 achieved 72.2% sensitivity, 85.7% specificity, and 79.5% accuracy for discriminating DLB from AD”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Suzuki et al., 2022
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- Authors
- Suzuki, Y., Suzuki, M., Shigenobu, K., Shinosaki, K., Aoki, Y., Kikuchi, H., Baba, T., Hashimoto, M., Araki, T., Johnsen, K., Ikeda, M., & Mori, E.
- Affiliations
- The University of Osaka (JP)
- Asakayama General Hospital (JP)
- Nippon Life Hospital (JP)
- Tohoku Medical and Pharmaceutical University (JP)
- Sendai Nishitaga National Hospital (JP)
- Kindai University (JP)
- Osaka University Hospital (JP)
- Corresponding author
- Mori, Etsuro
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-03-31
- Volume
- 17
- Issue
- 3
- Pages
- e0265484
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC8970386
- OpenAlex
- W4221101708
- Cited by
- 10
- References
- 47
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- JSPS KAKENHI (JP19K17062)
- Mentis Cura
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- dementia with Lewy bodies, Alzheimer's disease, differential diagnosis, quantitative EEG, machine learning classifiers, EEG biomarkers, donepezil, dementia diagnosis
- MeSH terms
- Machine Learning, Algorithms, Alzheimer Disease, Diagnosis, Differential, Electroencephalography, Humans, Prospective Studies, Lewy Body Disease
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