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Related Concept Videos

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...
Transient Ischemic Attack l: Introduction01:26

Transient Ischemic Attack l: Introduction

A transient ischemic attack (TIA) is a brief episode of neurological dysfunction caused by a temporary, focal reduction in cerebral blood flow. Although symptoms resemble those of an ischemic stroke, the interruption in perfusion is short-lived and does not cause permanent infarction. TIAs are clinically important because they often serve as early warning events for future stroke.Mechanisms of Transient Cerebral IschemiaTransient cerebral ischemia may arise through several mechanisms. One...

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Related Experiment Video

Updated: May 11, 2026

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
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Developing a Predictive Model for Ischemic Stroke Onset Time Using Transfer Learning.

Yang Du1,2, Shuai Wang3, Weidong Wang1,2

  • 1Department of Neurology, West China School of Medicine, Sichuan University, Sichuan University Affiliated Chengdu Second People's Hospital, Chengdu, China.

European Neurology
|December 9, 2025
PubMed
Summary

A new AI model accurately predicts acute ischemic stroke (AIS) onset within 4.5 hours using DWI and FLAIR imaging, outperforming human assessment and aiding timely treatment decisions.

Keywords:
Deep learningMRI diffusionOnset timeStrokeTransfer learning

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Area of Science:

  • Neurology
  • Radiology
  • Artificial Intelligence

Background:

  • Accurate identification of acute ischemic stroke (AIS) patients within the 4.5-hour therapeutic window is critical for effective treatment.
  • Current visual assessment of diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences for determining time since stroke (TSS) has limitations, including inter-observer variability and reduced accuracy.
  • There is a need for more objective and accurate methods to assess the time since stroke onset.

Purpose of the Study:

  • To develop and evaluate a transfer-learning model for predicting AIS onset within the 4.5-hour therapeutic window.
  • To compare the performance of the developed AI model against human visual assessment using the DWI-FLAIR mismatch principle.

Main Methods:

  • A retrospective analysis of 266 AIS patients with known TSS who underwent pre-treatment imaging scans (DWI and FLAIR).
  • Development of a 3D ResNet-18 transfer-learning model, pretrained on the Kinetics dataset and adapted for DWI-FLAIR input.
  • Comparison of the model's performance against human visual assessment on a validation set, focusing on sensitivity, specificity, and AUC, particularly for partial DWI-FLAIR mismatch cases.

Main Results:

  • The 3D ResNet-18 model achieved high performance on the validation set: sensitivity 0.833, specificity 0.880, and AUC 0.929.
  • The AI model significantly outperformed human visual assessment (sensitivity 0.767, specificity 0.360, AUC 0.563).
  • The model correctly classified all 15 partial DWI-FLAIR mismatch cases, while human assessment classified only 4.

Conclusions:

  • The developed 3D ResNet-18 transfer-learning model demonstrates significant promise for accurately identifying AIS within the 4.5-hour therapeutic window.
  • The model shows superior performance compared to human visual assessment, especially in challenging partial DWI-FLAIR mismatch cases.
  • Further multi-center validation is required before clinical implementation of this AI tool for AIS treatment decisions.