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Cognitive Dysfunction

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A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.

Translational psychiatry
Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment eff...

Predictive deep learning models for cognitive risk using accessible data.

Bioscience trends
The early detection of mild cognitive impairment (MCI) is crucial to preventing the progression of dementia. However, it necessitates that patients voluntarily undergo cognitive function tests, which may be too late if symptoms are only recognized on...

Large Language Models and Healthcare Alliance: Potential and Challenges of Two Representative Use Cases.

Annals of biomedical engineering
Large language models (LLMS) emerge as the most promising Natural Language Processing approach for clinical practice acceleration (i.e., diagnosis, prevention and treatment procedures). Similarly, intelligent conversational systems that leverage LLMS...

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...