ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2021
Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 11 citations in OpenAlex.
- Reproducibility-driven discovery and systematic benchmarking reveal a robust cerebrospinal fluid proteomic signature in Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026Article
- Distinct neurostructural, cognitive, and neuropsychiatric associations of plasma p-tau217, and Aβ42/40 in Parkinson's disease and aging cohorts.Frontiers in aging neuroscience · 2026Article
- Generating MRI-Derived CSF Proxy-Markers to Predict and Visualize Alzheimer's Disease Progression.Human brain mapping · 2025Article
- Assessment of localized brain regions correlated with MMSE using VBM analysis of structural MRI in a Japanese sample.Neuroimage. Reports · 2025Article
- The role of m6A modification in the risk prediction and Notch1 pathway of Alzheimer's disease.iScience · 2024Article
- Association of Arterial Stiffness and Atherosclerotic Burden With Brain Structural Changes Among Japanese Men.Journal of the American Heart Association · 2023Article
- Biomarkers for dementia in Latin American countries: Gaps and opportunities.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Machine learning of brain structural biomarkers for Alzheimer's disease (AD) diagnosis, prediction of disease progression, and amyloid beta deposition in the Japanese population.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
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5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
introductionWe developed machine learning (ML) designed to analyze structural brain magnetic resonance imaging (MRI), and trained it on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In this study, we verified its utility in the Japanese population.
methodsA total of 535 participants were enrolled from the Japanese ADNI database, including 148 AD, 152 normal, and 235 mild cognitive impairment (MCI). Probability of AD was expressed as AD likelihood scores (ADLS).
resultsThe accuracy of AD diagnosis was 88.0% to 91.2%. The accuracy of predicting the disease progression in non-dementia participants over a 3-year observation was 76.0% to 79.3%. More than 90% of the participants with low ADLS did not progress to AD within 3 years. In the amyloid positron emission tomography (PET)-positive MCI, the hazard ratio of progression was 2.39 with low ADLS, and 5.77 with high ADLS. When high ADLS was defined as N+ and Pittsburgh compound B (PiB) PET positivity was defined as A+, the time to disease progression for 50% of MCI participants was 23.7 months in A+N+, whereas it was 52.3 months in A+N-.
conclusionThese results support the feasibility of our ML for the diagnosis of AD and prediction of the disease progression.
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