#alzheimers-disease News & Analysis

5 articles tagged with #alzheimers-disease. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 117/10

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Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data

Researchers developed an attention-enhanced machine learning framework using ordinal regression to automate Alzheimer's disease severity staging by integrating MRI scans with clinical and genetic data. The multimodal ordinal model achieved 97% adjacent-stage accuracy and stronger agreement with clinical assessments than existing approaches, offering a scalable tool for neurodegenerative disease diagnosis.

AIBullisharXiv – CS AI · Jun 47/10

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Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset

Researchers developed an explainable machine learning model using XGBoost to detect Alzheimer's disease stages from routine clinical assessments, achieving 98.2% accuracy on three-class classification (normal cognition, mild cognitive impairment, and Alzheimer's disease). The model uses SHAP analysis to provide interpretable feature importance, identifying clinical biomarkers like CDR Global and MMSE as key predictors.

AINeutralarXiv – CS AI · Jun 236/10

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Delta-Diffusion: Modeling Longitudinal Brain Amyloid-PET Trajectories via Conditional Poisson Diffusion Bridge

Researchers introduce Delta-Diffusion, a novel AI framework using conditional Poisson Diffusion Bridges to synthesize longitudinal brain PET imaging for tracking amyloid accumulation in neurodegenerative diseases. The method addresses limitations of existing generative models by anchoring predictions to baseline patient scans and incorporating clinical progression patterns, potentially reducing the need for costly repeated imaging procedures.

AINeutralarXiv – CS AI · Jun 96/10

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Transition-Based Digital Twin Modelling for Alzheimer's Disease under Sparse Longitudinal Data

Researchers have developed a personalized digital twin framework for predicting Alzheimer's disease progression using multimodal longitudinal data from the ADNI database. The model employs transition-based and sequence-based approaches to capture clinical changes across sparse, irregular patient visits, achieving higher accuracy with local transition modeling while enabling patient-specific what-if scenario analysis.

AINeutralarXiv – CS AI · May 276/10

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CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies

Researchers developed CSV-ViT, a Vision Transformer model that uses variable-sized cortical surface patches to detect Alzheimer's disease pathologies from structural MRI scans. The method outperforms existing surface-based models and could enable earlier AD diagnosis through non-invasive imaging, potentially reducing reliance on costly PET scans and invasive cerebrospinal fluid testing.