Neurology

DCA-Enhanced Alzheimer's detection with shearlet and deep learning integration.

Alzheimer's dementia (AD) is a neurodegenerative disorder that affects the central nervous system, causing the cells to stop working or die. The quality of life for individuals with AD steadily declines over time. While current treatments can relieve symptoms, a definitive cure remains elusive. However, technological advancements in machine learning (ML) and deep learning (DL) have opened up new p...

Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.

The prevention and early warning of foot ulcers are crucial in diabetic care; however, early microvascular lesions are difficult to detect and often diagnosed at later stages, posing serious health risks. Infrared thermal imaging, as a rapid and non-contact clinical examination technology, can sensitively detect hidden neuropathy and vascular lesions for early intervention. This review provides an...

Design and Evaluation of Augmented Reality-Enhanced Robotic System for Epidural Interventions.

The epidural injection is a medical intervention to inject therapeutics directly into the vicinity of the spinal cord for pain management. Because of ...

Random survival forest model for early prediction of Alzheimer's disease conversion in early and late Mild cognitive impairment stages.

With a clinical trial failure rate of 99.6% for Alzheimer's Disease (AD), early diagnosis is critical. Machine learning (ML) models have shown promisi...

A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment.

BACKGROUND: Alzheimer's Disease (AD) poses a major challenge as a neurodegenerative disorder, and early detection is critical for effective interventi...

Multimodal Alzheimer's disease classification through ensemble deep random vector functional link neural network.

Alzheimer's disease (AD) is a condition with a complex pathogenesis, sometimes hereditary, characterized by the loss of neurons and synapses, along wi...

Artificial intelligence in emergency neuroradiology: Current applications and perspectives.

Emergency neuroradiology provides rapid diagnostic decision-making and guidance for management for a wide range of acute conditions involving the brai...

Photoplethysmography as a noninvasive surrogate for microneurography in measuring stress-induced sympathetic nervous activation - A machine learning approach.

The sympathetic nervous system (SNS) is essential for the body's immediate response to stress, initiating physiological changes that can be measured t...

Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.

This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephal...

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitati...

Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine lea...

Time-Frequency functional connectivity alterations in Alzheimer's disease and frontotemporal dementia: An EEG analysis using machine learning.

OBJECTIVE: Alzheimer's disease (AD) and frontotemporal dementia (FTD) are prevalent neurodegenerative diseases characterized by altered brain function...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hindering their ability to live healthy lives and partic...

Effectiveness of robot-assisted training in adults with Parkinson's disease: a systematic review and meta-analysis.

AIM: This work aimed to update and summarize the existing evidence on the effectiveness of robot-assisted training (RAT) in adults with Parkinson's di...

Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

BACKGROUND: Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. ...

Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.

Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, he...

Early detection of Alzheimer's disease in structural and functional MRI.

OBJECTIVES: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocamp...

Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms.

Alzheimer's disease (AD) is a serious neurodegenerative disorder that causes incurable and irreversible neuronal loss and synaptic dysfunction. The pr...

ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images.

One of the most familiar types of disease is Alzheimer's disease (AD) and it mainly impacts people over the age limit of 60. AD causes irreversible br...

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determi...