AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Early Diagnosis

Showing 351 to 360 of 435 articles

Clear Filters

Discriminative analysis of Parkinson's disease based on whole-brain functional connectivity.

PloS one
Recently, there has been an increasing emphasis on applications of pattern recognition and neuroimaging techniques in the effective and accurate diagnosis of psychiatric or neurological disorders. In the present study, we investigated the whole-brain...

Bridging a translational gap: using machine learning to improve the prediction of PTSD.

BMC psychiatry
BACKGROUND: Predicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivor...

Early detection of Alzheimer's disease using deep learning methods.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to analyze multimodal datasets, including clinical, biomarker, and neuroimaging data, using hybrid deep le...

Dynamic and Static Structure-Function Coupling With Machine Learning for the Early Detection of Alzheimer's Disease.

Human brain mapping
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to t...

Virtual reality and artificial intelligence: the future of mental health. A narrative review.

Recenti progressi in medicina
In recent years, the use of artificial intelligence (AI) and virtual reality (VR) in the psychiatric field has been rapidly developing. This narrative review seeks to provide insight into how these technologies may be used in psychiatric disorders. V...

New Method of Early RRMS Diagnosis Using OCT-Assessed Structural Retinal Data and Explainable Artificial Intelligence.

Translational vision science & technology
PURPOSE: The purpose of this study was to provide the development of a method to classify optical coherence tomography (OCT)-assessed retinal data in the context of automatic diagnosis of early-stage multiple sclerosis (MS) with decision explanation.

Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Mild cognitive impairment and early-stage dementia significantly impact healthcare utilization and costs, yet more than half of affected patients remain underdiagnosed. This study leverages audio-recorded patient-nurse verbal communicatio...

[Artificial intelligence in assessment of individual risks of age-related macular degeneration progression].

Vestnik oftalmologii
Age-related macular degeneration (AMD) is a progressive degenerative retinal disease and a leading cause of blindness in older adults worldwide. According to numerous studies, the number of affected individuals reached 196 million in 2020, with proje...

A Dynamic Model for Early Prediction of Alzheimer's Disease by Leveraging Graph Convolutional Networks and Tensor Algebra.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Alzheimer's disease (AD) is a neurocognitive disorder that deteriorates memory and impairs cognitive functions. Mild Cognitive Impairment (MCI) is generally considered as an intermediate phase between normal cognitive aging and more severe conditions...

Computerized Decision Support System and Fuzzy Logic Rules for Early Diagnosis of Pesticide-Induced Diseases.

Critical reviews in biomedical engineering
Many reflexologists employ outdated concepts that do not align with modern anatomy, physiology, and biophysics. Those concepts undermine physicians' confidence in their diagnosis. This study aims to improve the quality of medical care for workers in ...