ArticleFrontiers in aging neuroscience2025
Multimodal radiomics of cerebellar subregions for machine learning-driven Alzheimer's disease diagnosis.
Article in Frontiers in aging neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Review
- Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.BMC medical informatics and decision making · 2026Article
- Graded normobaric hypoxia alters cerebral oxygenation and cognition in middle-aged adults: a single-blind counterbalanced randomized crossover trial.GeroScience · 2026Article
- Multimodal MRI Radiomics and Machine Learning Identify Node-Level Structural and Functional Alterations in HIV-Associated Asymptomatic Neurocognitive Impairment.Neuropsychiatric disease and treatment · 2026Article
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8 authors.
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Abstract
Objective: This study aimed to develop a machine learning model based on multimodal radiomics features from cerebellar subregions, utilizing the complementarity of cerebellar structural and metabolic imaging data for accurate diagnosis of Alzheimer's disease (AD). Methods: A total of 164 cognitively normal (CN) subjects and 146 AD patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were included. All participants had 3DT1-weighted magnetic resonance imaging (3DT1W MRI) and [ Results: All three models could effectively diagnose AD, with the multimodal model showing the best performance. In the independent test set, the multimodal model achieved an AUC of 0.903, which was higher than the single-modality models based on [ Conclusion: The multimodal radiomics model based on cerebellar subregions, which integrates [
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