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  • AI-based differential diagnosis of dementia etiologies on . . .
    We present an AI model designed for differential dementia diagnosis by processing a range of multimodal data
  • Early detection of Alzheimers disease using deep learning . . .
    Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep learning frameworks to improve predictive accuracy
  • AI-based differential diagnosis of dementia etiologies on . . .
    This repository contains the implementation of a deep learning framework for the differential diagnosis of dementia etiologies using multi-modal data Using data from $9$ distinct cohorts totalling $51,269$ participants, we developed an algorithmic framework, utilizing transformers and self-supervised learning, to execute differential diagnoses
  • Early detection of dementia using artificial intelligence and . . .
    The main findings of the study were that Alzheimer’s disease and Mild Cognitive Impairment (MCI) are the most researched dementia types in the field; typical choice for dementia detection is Machine Learning (ML) methods; the most popular modalities combination is T1w + Fluorodeoxyglucose - Positron Emission Tomography (FDG-PET); accuracy
  • Automatic Detection of Alzheimer’s Disease with Multi-Modal . . .
    We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer’s Disease (AD), based on two different types of brain MRI scans We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans
  • Artificial intelligence for differential diagnosis of dementia
    Using routinely collected multimodal clinical data, we developed an artificial intelligence (AI) model to identify dementia and determine factors causing it, including mixed dementias and





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