Strokes

Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity.

Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible p...

Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports.

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neu...

Hemodynamic Characteristics and Outcomes of Pulmonary Hypertension in Patients Undergoing Tricuspid Valve Repair or Replacement.

BACKGROUND: The impact of pulmonary hypertension (PH) on outcomes after surgical tricuspid valve rep...

Predictors of Stroke Outcome Extracted from Multivariate Linear Discriminant Analysis or Neural Network Analysis.

AIM: The prediction of functional outcome is essential in the management of acute ischemic stroke pa...

An Exoneuromusculoskeleton for Self-Help Upper Limb Rehabilitation After Stroke.

This article presents a novel electromyography (EMG)-driven exoneuromusculoskeleton that integrates ...

The impact of anticoagulant and antiplatelet drugs therapy on perioperative outcomes of purely off-clamp robot-assisted partial nephrectomy: a single-center experience.

A clinically applicable deep-learning model for detecting intracranial aneurysm in computed tomography angiography images.

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recom...

Application of a machine learning algorithm for detection of atrial fibrillation in secondary care.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases r...

Artificial Intelligence and Acute Stroke Imaging.

Artificial intelligence technology is a rapidly expanding field with many applications in acute stro...

Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.

Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the eme...

Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death.

Despite the salient benefits of the intravenous tissue plasminogen activator (tPA), symptomatic intr...

Machine learning to predict mortality after rehabilitation among patients with severe stroke.

Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke...

Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely ...

Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.

Machine learning (ML)-based algorithms for cardiovascular disease (CVD) risk assessment have shown p...

Ischemic Lesion Segmentation using Ensemble of Multi-Scale Region Aligned CNN.

The first and foremost step in the diagnosis of ischemic stroke is the delineation of the lesion fro...

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibit...

Functional Testing for Tranexamic Acid Duration of Action Using Modified Viscoelastometry.

INTRODUCTION: Tranexamic acid (TXA) is the standard medication to prevent or treat hyperfibrinolysis...

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.

This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying ...

Multimodal magnetic resonance imaging correlates of motor outcome after stroke using machine learning.

This study applied machine learning regression to predict motor function after stroke based on multi...

Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network.

Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke...

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients.

Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA)...