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Related Concept Videos

Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
Preload refers to the initial elongation of the cardiac myocytes before contraction and is related to the volume of blood filling the heart at the end of diastole, or end-diastolic volume. The...
Acute Coronary Syndrome IV: Interprofessional Care01:28

Acute Coronary Syndrome IV: Interprofessional Care

IntroductionThe management of Acute Coronary Syndrome (ACS) aims to minimize myocardial damage, preserve myocardial function, and prevent complications.Initial ManagementInpatient management involves continuous cardiac monitoring, preferably in an ICU, focusing on blood pressure, serum sodium, potassium, and creatinine levels, and urine output. Ongoing pharmacologic management is crucial for stabilizing the patient.Supplemental Oxygen: Administer supplemental oxygen if oxygen saturation is...
Ischemic Stroke l: Introduction01:15

Ischemic Stroke l: Introduction

Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.
Ischemic Stroke ll: Pathophysiology01:15

Ischemic Stroke ll: Pathophysiology

An ischemic stroke occurs when a cerebral blood vessel becomes obstructed, most often by a thrombus or embolus, interrupting the delivery of oxygen and glucose to brain tissue. Because neurons rely on continuous aerobic metabolism, energy failure begins within minutes of reduced perfusion. The region receiving the least blood flow becomes the infarct core, an area of irreversible cellular death. Surrounding this core lies the penumbra, a zone of hypoperfused but still viable tissue that is...
Hemorrhagic Stroke l: Introduction01:17

Hemorrhagic Stroke l: Introduction

A hemorrhagic stroke is an acute neurological event that occurs when a weakened cerebral blood vessel ruptures, allowing blood to accumulate within or around the brain. The sudden release of blood forms a focal hematoma that increases intracranial pressure, displaces neural tissue, and can obstruct cerebrospinal fluid pathways. These effects may be compounded by intraventricular extension of the hemorrhage, cerebral edema, or compression of adjacent structures, all of which contribute to...

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Related Experiment Video

Updated: Jun 14, 2026

Optimized System for Cerebral Perfusion Monitoring in the Rat Stroke Model of Intraluminal Middle Cerebral Artery Occlusion
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The stroke outcome optimization project: Acute ischemic strokes from a comprehensive stroke center.

John Absher1,2,3, Sarah Goncher4, Roger Newman-Norlund5

  • 1University of South Carolina School of Medicine, Greenville, SC, 29605, USA. absher@mailbox.sc.edu.

Scientific Data
|August 2, 2024
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Summary

This study shares a large dataset of acute ischemic stroke brain MRIs. Machine learning models can predict stroke severity using this imaging data, aiding research and clinical tools.

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Area of Science:

  • Neurology
  • Radiology
  • Medical Imaging
  • Machine Learning

Background:

  • Stroke is a major cause of disability, necessitating advanced diagnostic tools.
  • Magnetic Resonance Imaging (MRI) is crucial for acute stroke management.
  • Publicly available, diverse datasets are vital for developing robust machine learning models in healthcare.

Purpose of the Study:

  • To create and share a comprehensive dataset of acute ischemic stroke brain MRI scans.
  • To enable the development of machine learning algorithms for stroke lesion identification, brain health assessment, and prognosis.
  • To provide a benchmark dataset and reproducible methods for stroke research.

Main Methods:

  • Acquisition of clinical MRI data (diffusion-weighted, FLAIR, T1-weighted) from 1715 individuals, including 1461 with acute ischemic stroke.
  • Collection of demographic and impairment data (NIH Stroke Scale/Score - NIHSS) for 1106 stroke survivors.
  • Development and validation of machine learning models to predict stroke severity using imaging data.

Main Results:

  • A large, diverse dataset of acute ischemic stroke MRI scans and associated clinical data is now publicly available.
  • Machine learning models successfully predicted stroke severity (NIHSS) using the provided imaging data.
  • The study provides reproducible scripts, facilitating further research and validation.

Conclusions:

  • The shared dataset and methods support the advancement of AI-driven stroke diagnosis and management.
  • This resource can accelerate the development of more accurate and generalizable machine learning tools for stroke prognosis.
  • Public data sharing and reproducible research are essential for progress in computational neuroimaging.