AIMC Topic: Thrombolytic Therapy

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Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.

Academic radiology
RATIONALE AND OBJECTIVES: Intravenous thrombolysis decision-making and obtaining of consent would be assisted by an individualized risk-benefit ratio. Deep learning (DL) models may be able to assist with this patient selection.

Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study.

International journal of medical informatics
OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-specific electronic medical record (EMR) interface powered by natural language processing (NLP) techn...

Robotic telepresence versus standardly supervised stroke alert team assessments.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
BACKGROUND: Telemedicine has created access to emergency stroke care for patients in all communities, regardless of geography. We hypothesized that there is no difference in speed of assessment between vascular neurologist (VN) robotic telepresence a...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment prognosis of early neurological deterioration (END) in patients with ultra-early ischemic stroke after...

Estimating individualized effectiveness of receiving successful recanalization for ischemic stroke cases using machine learning techniques.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Directly measuring the causal effect of mechanical thrombectomy (MT) for each ischemic stroke patient remains challenging, as it is impossible to observe the outcomes for both with and without successful recanalization in the same individ...

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. A comprehensive search was conducted across PubMed, Web of...

[Saved myocardium in acute ST-segment elevation myocardial infarction post-reperfusion: Analysis by cardiac magnetic resonance].

Revista medica de Chile
OBJECTIVE: To quantify by cardiovascular magnetic resonance the salvaged myocardium in the myocardium supplied by the infarct-related artery in reperfused and non-reperfused patients with a first ST-segment elevation myocardial infarction (STEMI).

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...