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Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer's Disease Using Adaptive Weight Selection.
Diagnostics (Basel)
December 2024
Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
Background: Alzheimer's disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention.
View Article and Find Full Text PDFMultilingual Prediction of Cognitive Impairment with Large Language Models and Speech Analysis.
Brain Sci
December 2024
School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA.
Background: Cognitive impairment poses a significant global health challenge, emphasizing the critical need for early detection and intervention. Traditional diagnostics like neuroimaging and clinical evaluations are often subjective, costly, and inaccessible, especially in resource-poor settings. Previous research has focused on speech analysis primarily conducted using English data, leaving multilingual settings unexplored.
View Article and Find Full Text PDFEffects of Route Complexity and Lighting on Route Following in Alzheimer's Disease and Posterior Cortical Atrophy.
Brain Sci
November 2024
Dementia Research Centre, Department of Neurodegeneration, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
Objective: Visual processing deficits arising in dementia are associated with particular functional disability. This study aimed to investigate the effects of the built environment on mobility and navigation in people with dementia-related visual loss.
Methods: Participants with posterior cortical atrophy (PCA; "visual-variant Alzheimer's"; n = 11), typical Alzheimer's disease (tAD; N = 10), and controls (n = 13) repeatedly walked down routes within a simplified real-world setting.
Alzheimer's disease recognition using graph neural network by leveraging image-text similarity from vision language model.
Sci Rep
January 2025
Integrated Intelligence Research Section, Electronics and Telecommunications Research Institute, Daejeon, 34129, Republic of Korea.
Alzheimer's disease (AD), a progressive neurodegenerative condition, notably impacts cognitive functions and daily activity. One method of detecting dementia involves a task where participants describe a given picture, and extensive research has been conducted using the participants' speech and transcribed text. However, very few studies have explored the modality of the image itself.
View Article and Find Full Text PDFCausal Discovery with Generalized Linear Models through Peeling Algorithms.
J Mach Learn Res
January 2024
Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
This article presents a novel method for causal discovery with generalized structural equation models suited for analyzing diverse types of outcomes, including discrete, continuous, and mixed data. Causal discovery often faces challenges due to unmeasured confounders that hinder the identification of causal relationships. The proposed approach addresses this issue by developing two peeling algorithms (bottom-up and top-down) to ascertain causal relationships and valid instruments.
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