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Related Experiment Video

Updated: Sep 14, 2025

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke
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Explainable machine learning model for predicting functional outcomes in posterior circulation stroke after

Zhelv Yao1,2,3, Qiuhong Ji4, Xuefeng Zang5

  • 1Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Journal of Neurointerventional Surgery
|July 23, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict functional outcomes for patients with posterior circulation stroke (PCS) after endovascular thrombectomy (EVT). These tools aid in personalized risk assessment and treatment planning for better patient management.

Keywords:
InterventionReperfusionStrokeThrombectomy

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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Predicting functional outcomes in posterior circulation stroke (PCS) is vital for timely interventions.
  • Machine learning (ML) models can aid in predicting outcomes for patients undergoing endovascular thrombectomy (EVT).
  • This study focuses on developing and validating ML models for 3-month functional outcome prediction in PCS patients post-EVT.

Purpose of the Study:

  • To develop and validate machine learning models for predicting functional outcomes in PCS patients after EVT.
  • To identify key predictive features for functional outcomes.
  • To provide an accessible tool for clinical decision support.

Main Methods:

  • A derivation cohort of 202 PCS patients undergoing EVT was used for training and internal validation.
  • An external dataset of 54 patients was used for external validation.
  • Seven ML models were trained on preoperative features, with the best performing model further trained using intraoperative and postoperative data. Model interpretability was assessed using SHAP.

Main Results:

  • The Random Forest model showed the best performance.
  • The preoperative model achieved an AUC of 0.83 (test set) and 0.81 (external validation).
  • Incorporating intraoperative and postoperative features improved AUCs to 0.84/0.90 (test) and 0.83/0.90 (external validation). A web calculator is available.

Conclusions:

  • Interpretable ML models accurately predict functional outcomes in PCS patients post-EVT.
  • These models offer valuable insights for personalized risk stratification and perioperative management.
  • The models have potential for integration into clinical workflows to optimize patient care.