Neurology

Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy.

Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entropies and chaotic features. This study aims at examining the discriminative power of individual features and their combination in the diagnosis of a neu...

Deep Learning to Decipher the Progression and Morphology of Axonal Degeneration.

Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progression of AxD in cortical neurons using a novel microfluidic device together with a deep learning tool that we developed for the enhanced-throughput analy...

Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.

Virtual reality (VR)-based motor therapy is an emerging approach in neurorehabilitation. The combination of VR with electroencephalography (EEG) prese...

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.

Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorr...

A comparative study of anticoagulant/antiplatelet therapy among men undergoing robot-assisted radical prostatectomy: a prospective single institution study.

The present study aimed to assess the safety and efficacy of robot-assisted radical prostatectomy (RARP) in patients with prostate cancer (PCa) under ...

Deep learning methods for automatic segmentation of lower leg muscles and bones from MRI scans of children with and without cerebral palsy.

Cerebral palsy is a neurological condition that is known to affect muscle growth. Detailed investigations of muscle growth require segmentation of mus...

Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement.

Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict...

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI).

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is n...

Self-Supervised Longitudinal Neighbourhood Embedding.

Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases. Analyz...

Non-invasive diagnostic tool for Parkinson's disease by sebum RNA profile with machine learning.

Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic...

EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features.

Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is v...

Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants.

Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This s...

EEG-Based Personality Prediction Using Fast Fourier Transform and DeepLSTM Model.

In this paper, a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram (EEG) signals is impleme...

The Role of Surface Electromyography in Data Fusion with Inertial Sensors to Enhance Locomotion Recognition and Prediction.

Locomotion recognition and prediction is essential for real-time human-machine interactive control. The integration of electromyography (EMG) with mec...

Machine learning approaches applied in spinal pain research.

The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to h...

Impact of Bladder Neck Angle Measured by Postoperative Magnetic Resonance Imaging on Midterm Recovery of Urinary Continence in Prostate Cancer Patients Undergoing Robot-Assisted Radical Prostatectomy.

MRI has helped clarify the relationship between pelvic anatomical structures and functional outcomes after robot-assisted radical prostatectomy (RARP...

Improved Self-Organizing Map-Based Unsupervised Learning Algorithm for Sitting Posture Recognition System.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and...

Artificial Intelligence Trained by Deep Learning Can Improve Computed Tomography Diagnosis of Nontraumatic Subarachnoid Hemorrhage by Nonspecialists.

Subarachnoid hemorrhage (SAH) is a serious cerebrovascular disease with a high mortality rate and is known as a disease that is hard to diagnose becau...

Automated Collateral Flow Assessment in Patients with Acute Ischemic Stroke Using Computed Tomography with Artificial Intelligence Algorithms.

BACKGROUND: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) ...

The emergence of machine learning in auditory neural impairment: A systematic review.

Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment of the auditory neural impairment may impose chal...