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Ischemic Stroke ll: Pathophysiology

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...

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Related Experiment Video

Updated: Jun 26, 2026

Non-invasive Imaging and Analysis of Cerebral Ischemia in Living Rats Using Positron Emission Tomography with 18F-FDG

Non-invasive Imaging and Analysis of Cerebral Ischemia in Living Rats Using Positron Emission Tomography with 18F-FDG

Published on: December 28, 2014

Cerebral ischemia detection using deep learning techniques.

Rafael Pastor-Vargas¹, Cristina Antón-Munárriz², Juan M Haut³

¹Communications and Control Systems, Computer Engineering Science Faculty, UNED, Calle Juan del Rosal 16, 28040 Madrid, Spain.

Health Information Science and Systems

|May 22, 2025

Summary

This summary is machine-generated.

Deep learning models can detect early stroke signs on non-contrast CT scans. DenseNet3D achieved 95% accuracy, aiding prompt diagnosis and intervention for cerebrovascular accident (stroke).

Keywords:

Cerebral ischemia Computed tomography Deep learning Ictus dataset Transfer learning

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Area of Science:

* Medical Imaging Analysis
* Artificial Intelligence in Healthcare
* Neurology

Background:

* Cerebrovascular accident (stroke) is a leading cause of death and disability.
* Early detection of stroke is critical for effective treatment and improved outcomes.
* Non-contrast computed tomography (NCCT) is the standard initial imaging modality for suspected stroke.

Purpose of the Study:

* To develop and evaluate deep learning models for identifying early stroke indicators on NCCT scans.
* To differentiate NCCT scans with and without signs of stroke using a binary classifier.
* To assess the performance of 3D convolutional neural networks (VGG3D, ResNet3D, DenseNet3D) for stroke detection.

Main Methods:

* Implementation of VGG3D, ResNet3D, and DenseNet3D deep learning architectures using 3D brain imaging data.
* Training and validation of models on a dataset of NCCT scans to identify subtle density alterations.
* Evaluation of model performance based on accuracy, sensitivity, and specificity.

Main Results:

* DenseNet3D demonstrated superior performance among the evaluated models.
* The DenseNet3D model achieved a training accuracy of 98% and a test accuracy of 95%.
* The study successfully identified early NCCT density changes indicative of stroke.

Conclusions:

* Deep learning, specifically DenseNet3D, shows significant potential for automated early stroke detection on NCCT.
* Accurate and rapid identification of stroke can facilitate timely medical intervention.
* This AI-driven approach can serve as a valuable tool for clinicians in emergency settings.