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Cognitive Dysfunction

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c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

PloS one
Alzheimer's disease (AD) is a neurodegenerative disorder that requires early diagnosis for effective management. However, issues with currently available diagnostic biomarkers preclude early diagnosis, necessitating the development of alternative bio...

The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Parkinson disease (PD) is a common neurodegenerative disease characterized by both motor and nonmotor symptoms. Cognitive impairment often occurs early in the disease and can persist throughout its progression, severely impacting patients...

Ensemble network using oblique coronal MRI for Alzheimer's disease diagnosis.

NeuroImage
Alzheimer's disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer's disease. Therefore, distinguishing between normal...

Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.

International journal of molecular sciences
Mild cognitive impairment (MCI) is a clinical condition characterized by a decline in cognitive ability and progression of cognitive impairment. It is often considered a transitional stage between normal aging and Alzheimer's disease (AD). This study...

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.

Neurology
BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We...

Leveraging machine learning for precision medicine: a predictive model for cognitive impairment in cholestasis patients.

BMC gastroenterology
BACKGROUND: Cholestasis, characterized by impaired bile flow, impacts cognitive function through systemic mechanisms, including inflammation and metabolic dysregulation. Despite its significance, targeted predictive models for cognitive impairment in...

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

Human brain mapping
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

Journal of biomedical informatics
OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and underlying pathophysiology. In this study, we propose a machine learning framework to identify subpheno...

Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.

BMC medical informatics and decision making
BACKGROUND: Cognitive impairment is common after a stroke, but it can often go undetected. In this study, we investigated whether using gait and dual tasks could help detect cognitive impairment after stroke.

Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI.

Human brain mapping
Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-...