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Cognitive Dysfunction

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Enhanced detection of mild cognitive impairment in Alzheimer's disease: a hybrid model integrating dual biomarkers and advanced machine learning.

BMC geriatrics
Alzheimer's disease (AD) is a complex, progressive, and irreversible neurodegenerative disorder marked by cognitive decline and memory loss. Early diagnosis is the most effective strategy to slow the disease's progression. Mild Cognitive Impairment (...

Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.

Alzheimer's research & therapy
BACKGROUND: Intracerebral amyloid β (Aβ) accumulation is considered the initial observable event in the pathological process of Alzheimer's disease (AD). Efficient screening for amyloid pathology is critical for identifying patients for early treatme...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Scientific reports
Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality of life. Early identification of at-risk individuals and timely intervention are essential. Neverthel...

A Dynamic Adaptive Ensemble Learning Framework for Noninvasive Mild Cognitive Impairment Detection: Development and Validation Study.

JMIR medical informatics
BACKGROUND: The prompt and accurate identification of mild cognitive impairment (MCI) is crucial for preventing its progression into more severe neurodegenerative diseases. However, current diagnostic solutions, such as biomarkers and cognitive scree...

Exploring the triglyceride-glucose index's role in depression and cognitive dysfunction: Evidence from NHANES with machine learning support.

Journal of affective disorders
BACKGROUND: Depression and cognitive impairments are prevalent among older adults, with evidence suggesting potential links to obesity and lipid metabolism disturbances. This study investigates the relationships between the triglyceride-glucose (TyG)...

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

Japanese journal of radiology
PURPOSE: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-weighted sequence, but is not a priority in routine brain examinations. We hypothesized that converting 3D MRI localizer (AutoAlign Head) images to MPR...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Biomedical physics & engineering express
Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicator of disease progression. However, many existing methods focus mainly on the image when processing b...

Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.

Journal of Alzheimer's disease : JAD
BACKGROUND: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or no...

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

Journal of Alzheimer's disease : JAD
BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understanding the neural correlates of MCI is crucial for elucidating its pathophysiology and developing effec...

Using machine learning models to identify severe subjective cognitive decline and related factors in nurses during the menopause transition: a pilot study.

Menopause (New York, N.Y.)
OBJECTIVE: This study aims to develop and validate a machine learning model for identifying individuals within the nursing population experiencing severe subjective cognitive decline (SCD) during the menopause transition, along with their associated ...