AIMC Topic: Cognitive Dysfunction

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Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images.

Ophthalmology. Retina
OBJECTIVE: We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images.

Comparing the performance of statistical, machine learning, and deep learning algorithms to predict time-to-event: A simulation study for conversion to mild cognitive impairment.

PloS one
Mild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, that is beyond what is expected due to normal aging. Detection of MCI is crucial for providing appropri...

Deep insights into MCI diagnosis: A comparative deep learning analysis of EEG time series.

Journal of neuroscience methods
BACKGROUND: Individuals in the early stages of Alzheimer's Disease (AD) are typically diagnosed with Mild Cognitive Impairment (MCI). MCI represents a transitional phase between normal cognitive function and AD. Electroencephalography (EEG) records c...

Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation.

BMC geriatrics
BACKGROUND: The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impair...

Robotic assessment of sensorimotor and cognitive deficits in patients with temporal lobe epilepsy.

Epilepsy & behavior : E&B
OBJECTIVE: Individuals with temporal lobe epilepsy (TLE) frequently demonstrate impairments in executive function, working memory, and/or declarative memory. It is recommended that screening for cognitive impairment is undertaken in all people newly ...

Text Dialogue Analysis for Primary Screening of Mild Cognitive Impairment: Development and Validation Study.

Journal of medical Internet research
BACKGROUND: Artificial intelligence models tailored to diagnose cognitive impairment have shown excellent results. However, it is unclear whether large linguistic models can rival specialized models by text alone.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Neural networks : the official journal of the International Neural Network Society
Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined wit...

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.

Neuroinformatics
Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble metho...

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture.

International journal of language & communication disorders
BACKGROUND: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early dete...

Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.

Journal of healthcare engineering
The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer's. Therefore, the det...