AIMC Topic: Cognitive Dysfunction
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Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.
Journal of biomedical informatics
Apr 1, 2025
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
Apr 1, 2025
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.
Structural and metabolic topological alterations associated with butylphthalide treatment in mild cognitive impairment: Data from a randomized, double-blind, placebo-controlled trial.
Psychiatry and clinical neurosciences
Mar 31, 2025
AIMS: Effective intervention for mild cognitive impairment (MCI) is key for preventing dementia. As a neuroprotective agent, butylphthalide has the potential to treat MCI due to Alzheimer disease (AD). However, the pharmacological mechanism of butylp...
A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.
Neurology
Mar 25, 2025
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...
Ensemble network using oblique coronal MRI for Alzheimer's disease diagnosis.
NeuroImage
Mar 25, 2025
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...
Class balancing diversity multimodal ensemble for Alzheimer's disease diagnosis and early detection.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 22, 2025
Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional ...
Leveraging machine learning for precision medicine: a predictive model for cognitive impairment in cholestasis patients.
BMC gastroenterology
Mar 18, 2025
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...
Stages prediction of Alzheimer's disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data.
Scientific reports
Mar 18, 2025
Detection of Alzheimer's Disease (AD) is critical for successful diagnosis and treatment, involving the common practice of screening for Mild Cognitive Impairment (MCI). However, the progressive nature of AD makes it challenging to identify its causa...
The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Mar 14, 2025
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...
Automatic Detection of Cognitive Impairment in Patients With White Matter Hyperintensity Using Deep Learning and Radiomics.
American journal of Alzheimer's disease and other dementias
Mar 14, 2025
White matter hyperintensity (WMH) is associated with cognitive impairment. In this study, 79 patients with WMH from hospital 1 were randomly divided into a training set (62 patients) and an internal validation set (17 patients). In addition, 29 WMH p...
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