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Ischemic Stroke l: Introduction

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: 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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Updated: Jun 13, 2026

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

Published on: August 18, 2015

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction.

Mohamed Sobhi Jabal¹, Olivier Joly², David Kallmes¹

¹Department of Radiology, Mayo Clinic, Rochester, MN, United States.

Frontiers in Neurology

|June 6, 2022

Summary

This summary is machine-generated.

Machine learning models predict stroke recovery after mechanical thrombectomy using clinical and imaging data. This approach aids in forecasting patient outcomes for acute ischemic stroke (AIS) treatment.

Keywords:

artificial intelligence ischemic stroke machine learning prediction model prognosis

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Published on: May 24, 2021

Area of Science:

Neurology and Medical Imaging
Artificial Intelligence in Medicine
Stroke Research

Background:

Mechanical thrombectomy significantly improves outcomes for acute ischemic stroke (AIS).
However, predicting functional recovery in all patients remains challenging despite successful reperfusion.
Accurate pre-interventional prediction of post-thrombectomy outcomes is crucial for patient management.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting functional outcomes at 3 months post-thrombectomy in AIS patients.
To utilize readily available clinical and auto-extractable radiological information for pre-interventional prediction.
To enhance the accuracy of prognostication for AIS patients undergoing mechanical thrombectomy.

Main Methods:

A retrospective analysis of 293 AIS patients who underwent mechanical thrombectomy at two centers.
Development of ML models to predict the modified Rankin Scale at 90 days (mRS-90) using combined clinical and imaging features.
Quantification of imaging biomarkers from non-contrast CT and CTA using automated software; SHAP for model interpretability.

Main Results:

The optimal ML model combined clinical and imaging features, achieving an AUC of 84% using Extreme Gradient Boosting (XGB).
Key predictors included age, NIHSS score, occlusion side, brain atrophy (CSF and lateral ventricle volume), early ischemic changes (ASPECTS), and collateral status.
The XGB model demonstrated strong performance in predicting dichotomized mRS-90.

Conclusions:

ML models integrating quantifiable CT/CTA features with clinical data offer a promising automated approach for pre-interventional stroke prognosis.
Interpretable ML models, like XGB with SHAP analysis, identify crucial factors influencing post-thrombectomy outcomes.
This predictive capability can personalize treatment strategies and improve patient outcomes in acute ischemic stroke.