AI Medical Compendium Journal:
Frontiers in neuroscience

Showing 1 to 10 of 35 articles

A recurrent YOLOv8-based framework for event-based object detection.

Frontiers in neuroscience
Object detection plays a crucial role in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on conventional frame-based RGB sensors. However, these sensors face challenges such as motion bl...

Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.

Frontiers in neuroscience
INTRODUCTION: Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture-a highly invasive method-to validate the diagnosis. Additionally, MRI is periodica...

NDUFA11 may be the disulfidptosis-related biomarker of ischemic stroke based on integrated bioinformatics, clinical samples, and experimental analyses.

Frontiers in neuroscience
BACKGROUND: Programmed cell death plays an important role in neuronal injury and death after ischemic stroke (IS), leading to cellular glucose deficiency. Glucose deficiency can cause abnormal accumulation of cytotoxic disulfides, resulting in disulf...

A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS.

Frontiers in neuroscience
INTRODUCTION: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement significantly impacts psychosocial, emotional, and physical health. A validated objective marker is however lacking to characterize and phenotype bulbar in...

Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.

Frontiers in neuroscience
INTRODUCTION: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early dia...

Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit.

Frontiers in neuroscience
INTRODUCTION: Delirium, frequently experienced by ischemic stroke patients, is one of the most common neuropsychiatric syndromes reported in the Intensive Care Unit (ICU). Stroke patients with delirium have a high mortality rate and lengthy hospitali...

Harnessing the potential of human induced pluripotent stem cells, functional assays and machine learning for neurodevelopmental disorders.

Frontiers in neuroscience
Neurodevelopmental disorders (NDDs) affect 4.7% of the global population and are associated with delays in brain development and a spectrum of impairments that can lead to lifelong disability and even mortality. Identification of biomarkers for accur...

Application of MRI image segmentation algorithm for brain tumors based on improved YOLO.

Frontiers in neuroscience
OBJECTIVE: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor ma...

Neurochallenges in smart cities: state-of-the-art, perspectives, and research directions.

Frontiers in neuroscience
Smart city development is a complex, transdisciplinary challenge that requires adaptive resource use and context-aware decision-making practices to enhance human functionality and capabilities while respecting societal and environmental rights, and e...

A machine learning-based predictive model for predicting early neurological deterioration in lenticulostriate atheromatous disease-related infarction.

Frontiers in neuroscience
BACKGROUND AND AIM: This study aimed to develop a predictive model for early neurological deterioration (END) in branch atheromatous disease (BAD) affecting the lenticulostriate artery (LSA) territory using machine learning. Additionally, it aimed to...