AIMC Topic: Cognitive Dysfunction

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Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning.

Medical image analysis
Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and cli...

Exploring Cognitive Dysfunction in Long COVID Patients: Eye Movement Abnormalities and Frontal-Subcortical Circuits Implications via Eye-Tracking and Machine Learning.

The American journal of medicine
BACKGROUND: Cognitive dysfunction is regarded as one of the most severe aftereffects following coronavirus disease 2019 (COVID-19). Eye movements, controlled by several brain areas, such as the dorsolateral prefrontal cortex and frontal-thalamic circ...

Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease.

International journal of neural systems
Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promisin...

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

Alzheimer's research & therapy
BACKGROUND: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by...

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

Journal of translational medicine
BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI...

Predicting the Rapid Progression of Mild Cognitive Impairment by Intestinal Flora and Blood Indicators through Machine Learning Method.

Neuro-degenerative diseases
INTRODUCTION: The aim of the work was to establish a prediction model of mild cognitive impairment (MCI) progression based on intestinal flora by machine learning method.

Retinal OCT biomarkers and their association with cognitive function-clinical and AI approaches.

Die Ophthalmologie
Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in mon...

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...