Neurology

Development and validation of a novel model for characterizing migraine outcomes within real-world data.

BACKGROUND: In disease areas with 'soft' outcomes (i.e., the subjective aspects of a medical condition or its management) such as migraine or depression, extraction and validation of real-world evidence (RWE) from electronic health records (EHRs) and other routinely collected data can be challenging due to how the data are collected and recorded. In this study, we aimed to define and validate a sc...

Experiences of patients who had a stroke and rehabilitation professionals with upper limb rehabilitation robots: a qualitative systematic review protocol.

INTRODUCTION: Emerging evidence suggests that robotic devices for upper limb rehabilitation after a stroke may improve upper limb function. For robotic upper limb rehabilitation in stroke to be successful, patients' experiences and those of the rehabilitation professionals must be considered. Therefore, this review aims to synthesise the available evidence on experiences of patients after a stroke...

Deep-learning analysis of micropattern-based organoids enables high-throughput drug screening of Huntington's disease models.

Organoids are carrying the promise of modeling complex disease phenotypes and serving as a powerful basis for unbiased drug screens, potentially offer...

Neurodynamic approaches for sparse recovery problem with linear inequality constraints.

This paper develops two neurodynamic approaches for solving the L-minimization problem with the linear inequality constraints. First, a centralized ne...

Internet of Things (IoT) Enables Robot-Assisted Therapy as a Home Program for Training Upper Limb Functions in Chronic Stroke: A Randomized Control Crossover Study.

OBJECTIVE: To compare the effects of using an Internet of things (IoT)-assisted tenodesis-induced-grip exoskeleton robot (TIGER) and task-specific mot...

A review of automated sleep disorder detection.

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effecti...

Predicting conversion to Alzheimer's disease in individuals with Mild Cognitive Impairment using clinically transferable features.

Patients with Mild Cognitive Impairment (MCI) have an increased risk of Alzheimer's disease (AD). Early identification of underlying neurodegenerative...

A Hierarchical Ensemble Deep Learning Activity Recognition Approach with Wearable Sensors Based on Focal Loss.

Abnormal activity in daily life is a relatively common symptom of chronic diseases, such as dementia. There will probably be a variety of repetitive a...

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions.

Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care...

Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.

PURPOSE: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentatio...

Development and External Validation of a Machine Learning Model for the Early Prediction of Doses of Harmful Intracranial Pressure in Patients with Severe Traumatic Brain Injury.

Treatment and prevention of elevated intracranial pressure (ICP) is crucial in patients with severe traumatic brain injury (TBI). Elevated ICP is asso...

Deep learning approaches based improved light weight U-Net with attention module for optic disc segmentation.

Glaucoma is a major cause of blindness worldwide, and its early detection is essential for the timely management of the condition. Glaucoma-induced an...

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.

Although current research aims to improve deep learning networks by applying knowledge about the healthy human brain and vice versa, the potential of ...

Spinal robotics in cervical spine surgery: a systematic review with key concepts and technical considerations.

OBJECTIVE: Spinal robotics for thoracolumbar procedures, predominantly employed for the insertion of pedicle screws, is currently an emerging topic in...

Effects of nerve-sparing procedures on surgical margins after robot-assisted radical prostatectomy.

BACKGROUND: Nerve-sparing (NS) techniques could potentially increase positive surgical margins after robot-assisted radical prostatectomy (RARP). Neve...

Deep Learning for Daily Monitoring of Parkinson's Disease Outside the Clinic Using Wearable Sensors.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashin...

Individual Identification by Late Information Fusion of EmgCNN and EmgLSTM from Electromyogram Signals.

This paper is concerned with individual identification by late fusion of two-stream deep networks from Electromyogram (EMG) signals. EMG signal has mo...

Neuroprotective and antioxidant activities of different polarity parts of the extracts of the leaf and rhizome from Yongzhou.

In order to make better use of the two local dominant plant resources of and from Yongzhou in Hunan province, the neuroprotective and antioxidant a...

Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning.

BACKGROUND AND PURPOSE: Treatment of acute ischemic stroke is heavily contingent upon time, as there is a strong relationship between time clock and t...

Multi-modality MRI for Alzheimer's disease detection using deep learning.

Diffusion tensor imaging (DTI) is a new technology in magnetic resonance imaging, which allows us to observe the insightful structure of the human bod...