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Thrombolytic Therapy

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Correlation between Immune-Inflammatory Markers and Clinical Features in Patients with Acute Ischemic Stroke.

Acta neurologica Taiwanica
OBJECTIVE: Chronic inflammatory processes involving the vascular wall may induce atherosclerosis. Immune-inflammatory processes proceed throughout all stages of acute stroke. We investigated the association of three immune-inflammatory markers, namel...

Thrombolysis of Pulmonary Emboli via Endobronchial Ultrasound-Guided Transbronchial Needle Injection.

The Annals of thoracic surgery
BACKGROUND: Endobronchial ultrasound-guided transbronchial needle injection (EBUS-TBNI) is a novel technique for treating peribronchial targets. The aim of this study was to evaluate preliminary feasibility of thrombolysis of pulmonary emboli via EBU...

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on...

Reperfusion Therapy in Acute Ischemic Stroke with Active Cancer: A Meta-Analysis Aided by Machine Learning.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: While the prevalence of active cancer patients experiencing acute stroke is increasing, the effects of active cancer on reperfusion therapy outcomes are inconclusive. Thus, we aimed to compare the safety and outcomes of reperfusion therap...

Alberta Stroke Program Early CT Score Calculation Using the Deep Learning-Based Brain Hemisphere Comparison Algorithm.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: The Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a promising tool for the evaluation of stroke expansion to determine suitability for reperfusion therapy. The aim of this study was to validate deep learning-based AS...

Deep Learning-Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset.

Contrast media & molecular imaging
This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic therapy for acute cerebral infarct with unknown time of onset. A total of 100 patients with acute cere...

Real-Time Ultrasound Doppler Tracking and Autonomous Navigation of a Miniature Helical Robot for Accelerating Thrombolysis in Dynamic Blood Flow.

ACS nano
Untethered small-scale robots offer great promise for medical applications in complex biological environments. However, challenges remain in the control and medical imaging of a robot for targeted delivery inside a living body, especially in flowing ...

Energy spectrum CT index-based machine learning model predicts the effect of intravenous thrombolysis in lower limbs.

Journal of applied clinical medical physics
To develop a noninvasive machine learning (ML) model based on energy spectrum computed tomography venography (CTV) indices for preoperatively predicting the effect of intravenous thrombolytic treatment in lower limbs. A total of 3492 slices containin...

A weakly supervised deep learning model integrating noncontrasted computed tomography images and clinical factors facilitates haemorrhagic transformation prediction after intravenous thrombolysis in acute ischaemic stroke patients.

Biomedical engineering online
BACKGROUND: Haemorrhage transformation (HT) is a serious complication of intravenous thrombolysis (IVT) in acute ischaemic stroke (AIS). Accurate and timely prediction of the risk of HT before IVT may change the treatment decision and improve clinica...

Development and validation of outcome prediction model for reperfusion therapy in acute ischemic stroke using nomogram and machine learning.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
OBJECTIVE: To develop logistic regression nomogram and machine learning (ML)-based models to predict 3-month unfavorable functional outcome for acute ischemic stroke (AIS) patients undergoing reperfusion therapy.