Strokes

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.

BACKGROUND: Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful b...

Effects of Robot-Assisted Gait Training with Body Weight Support on Gait and Balance in Stroke Patients.

This study investigated the effects of robot-assisted gait training with body weight support on gait and balance in stroke patients. The study participants comprised 24 patients diagnosed with stroke. Patients were randomly assigned to four groups of six: robot A, B, C, and non-robot. The body weight support (BWS) for the harness of the robot was set to 30% of the patient’s body weight in robot gr...

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm.

The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combine...

Machine learning model prediction of 6-month functional outcome in elderly patients with intracerebral hemorrhage.

Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional ...

Exploiting exercise electrocardiography to improve early diagnosis of atrial fibrillation with deep learning neural networks.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. It results from abnormal irregularities in the electrical performance of the...

Leveraging machine learning tools and algorithms for analysis of fruit fly morphometrics.

Analysis of landmark-based morphometric measurements taken on body parts of insects have been a useful taxonomic approach alongside DNA barcoding in i...

Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow.

Anticoagulant drugs are commonly prescribed to prevent hypercoagulable states in patients with venous thromboembolism. The choice of the most efficien...

Clinical Prediction Rule for Identifying the Stroke Patients who will Obtain Clinically Important Improvement of Upper Limb Motor Function by Robot-Assisted Upper Limb.

BACKGROUND: The number of studies on the characteristics of patients with stroke who would benefit from robot-assisted upper limb rehabilitation is li...

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning.

It is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke d...

Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search.

The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely...

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.

Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It...

Effect of an artificial intelligence-assisted tool on non-valvular atrial fibrillation anticoagulation management in primary care: protocol for a cluster randomized controlled trial.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmia diseases. Thromboembolic prophylaxis plays an essential role in AF t...

Interpretability Analysis of One-Year Mortality Prediction for Stroke Patients Based on Deep Neural Network.

Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has...

Telerobotic neurovascular interventions with magnetic manipulation.

Advances in robotic technology have been adopted in various subspecialties of both open and minimally invasive surgery, offering benefits such as enha...

Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose.

In post-stroke motor rehabilitation, treatment dose description is estimated approximately. The aim of this retrospective study was to quantify the tr...

An Artificial Intelligence-Enhanced Blood Pressure Monitor Wristband Based on Piezoelectric Nanogenerator.

Hypertensive patients account for about 16% to 37% of the global population, and about 9.4 million people die each year from hypertension and its comp...

Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements.

BACKGROUND: Right atrial (RA) area predicts mortality in patients with pulmonary hypertension, and is recommended by the European Society of Cardiolog...

Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.

OBJECTIVE: We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approach...

Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.

Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (A...

Multistage Spatio-Temporal Networks for Robust Sketch Recognition.

Sketch recognition relies on two types of information, namely, spatial contexts like the local structures in images and temporal contexts like the ord...