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Related Experiment Video

Updated: Jun 28, 2026

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

First-Pass Reperfusion After Endovascular Thrombectomy: A Real-World Analysis with Explainable Machine Learning for

Ismail Dilek1, Ali Şahin2

  • 1Department of Radiology, Selçuk University Faculty of Medicine Hospital, Selçuklu Konya, Turkey.

Journal of Neuroradiology = Journal De Neuroradiologie
|June 26, 2026
PubMed
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This study developed an explainable machine learning model to predict first-pass reperfusion (FPE) in stroke patients undergoing endovascular thrombectomy, identifying key predictors like collateral status and blood pressure.

Area of Science:

  • Neurology
  • Medical Imaging
  • Machine Learning in Healthcare

Background:

  • Endovascular thrombectomy is a critical treatment for acute ischemic stroke.
  • Optimizing first-pass reperfusion (FPE) is crucial for patient outcomes.
  • Predictive tools for intra-procedural decision support are needed.

Purpose of the Study:

  • To identify determinants of FPE in endovascular thrombectomy.
  • To develop an explainable machine learning (ML) framework for predicting FPE.
  • To provide intra-procedural decision support for clinicians.

Main Methods:

  • Retrospective analysis of 204 acute ischemic stroke patients treated with endovascular thrombectomy.
  • Development and validation of six ML models using clinical, imaging, and procedural variables.
Keywords:
Acute ischemic strokeEndovascular thrombectomyExplainable artificial intelligenceFirst-pass reperfusionMachine learning

Related Experiment Videos

Last Updated: Jun 28, 2026

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

  • Application of SHAP and partial dependence analyses for model interpretability.

    • Main Results:

    • First-pass reperfusion (FPE) was achieved in 46.08% of patients.
    • Favorable collateral status, lower systolic blood pressure, and higher ASPECTS scores were associated with FPE.
    • Explainable ML identified collateral circulation, systolic blood pressure, hypertension, and age as key predictors.

    Conclusions:

    • An internally validated, explainable ML framework can estimate FPE likelihood using routine variables.
    • Findings are preliminary due to limited events and lack of external validation.
    • Further multicenter validation is required before clinical implementation.