Neurology

Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging.

Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the futu...

A prospective cohort study of the accuracy and safety of robot-assisted minimally invasive spinal surgery.

BACKGROUND: Robot-assisted open surgery (RA-OS) is now commonly used in traditional open-exposure spinal screw placement surgery. With the help of robots, robot-assisted minimally invasive surgery (RA-MIS) can achieve less bleeding and less tissue damage in percutaneous screw insertion. While the research comparing the safety and accuracy of screw placement between RA-MIS and RA-OS is insufficient...

Blockchain-Based Deep Learning to Process IoT Data Acquisition in Cognitive Data.

Remote health monitoring can help prevent disease at the earlier stages. The Internet of Things (IoT) concepts have recently advanced, enabling omnipr...

The spectrum of data sharing policies in neuroimaging data repositories.

Sharing data is a scientific imperative that accelerates scientific discoveries, reinforces open science inquiry, and allows for efficient use of publ...

Automated detection of clinical depression based on convolution neural network model.

As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection ...

Safety and efficacy of a novel robotic transcranial doppler system in subarachnoid hemorrhage.

Delayed cerebral ischemia (DCI) secondary to vasospasm is a determinate of outcomes following non-traumatic subarachnoid hemorrhage (SAH). SAH patient...

Raster plots machine learning to predict the seizure liability of drugs and to identify drugs.

In vitro microelectrode array (MEA) assessment using human induced pluripotent stem cell (iPSC)-derived neurons holds promise as a method of seizure a...

Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging.

The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebe...

The Predictive Value of Dynamic Intrinsic Local Metrics in Transient Ischemic Attack.

BACKGROUND: Transient ischemic attack (TIA) is known as "small stroke." However, the diagnosis of TIA is currently difficult due to the transient symp...

Semantic Integration of Multi-Modal Data and Derived Neuroimaging Results Using the Platform for Imaging in Precision Medicine (PRISM) in the Arkansas Imaging Enterprise System (ARIES).

Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most dat...

Rupture risk prediction of cerebral aneurysms using a novel convolutional neural network-based deep learning model.

BACKGROUND: Cerebral aneurysms should be treated before rupture because ruptured aneurysms result in serious disability. Therefore, accurate predictio...

Brain Network Organization Following Post-Stroke Neurorehabilitation.

Brain network analysis can offer useful information to guide the rehabilitation of post-stroke patients. We applied functional network connection mode...

Scalp EEG-Based Pain Detection Using Convolutional Neural Network.

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detec...

Improved Stroke Care in a Primary Stroke Centre Using AI-Decision Support.

BACKGROUND: Patient selection for reperfusion therapies requires significant expertise in neuroimaging. Increasingly, machine learning-based analysis ...

Is robot-assisted gait training intensity a determinant of functional recovery early after stroke? A pragmatic observational study of clinical care.

Gait rehabilitation is a critical factor in functional recovery after a stroke. The aim of this pragmatic observational study was to identify the opti...

Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network.

Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can...

Geometric Deep Learning for Subject Independent Epileptic Seizure Prediction Using Scalp EEG Signals.

Recently, researchers in the biomedical community have introduced deep learning-based epileptic seizure prediction models using electroencephalograms ...

Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model.

According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function...

High Plasma Concentration of Apolipoprotein C-III Confers an Increased Risk of Cerebral Ischemic Events on Cardiovascular Patients Anticoagulated With Warfarin.

INTRODUCTION: Apolipoprotein C-III (Apo CIII) is a crucial regulator of triglyceride-rich lipoproteins (TRLs) and influences the risk of cardiovascula...

Monitoring ecosystem services in the Guangdong-Hong Kong-Macao Greater Bay Area based on multi-temporal deep learning.

Assessment of ecosystem service supply and demand, as well as the budgets of ecosystem service supply and demand, is the basis of scientific urban pla...