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Related Experiment Video

Updated: Jul 12, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

A classification model for concurrent posterior circulation infarction in vertebrobasilar dolichoectasia using

Yilan Li1, Qiang Zhang2, Xujie Wang3

  • 1Department of Radiology, Tianjin Fourth Central Hospital, Tianjin, China.

Clinical Neurology and Neurosurgery
|July 9, 2026
PubMed
Summary

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Researchers developed a machine learning model to identify posterior circulation infarction (PCI) in patients with vertebrobasilar dolichoectasia (VBD). This tool aids in classifying concurrent PCI, improving patient management.

Area of Science:

  • Neurology
  • Medical Imaging
  • Machine Learning

Background:

  • Vertebrobasilar dolichoectasia (VBD) is a cerebrovascular condition often associated with posterior circulation infarction (PCI).
  • Lack of tools to classify concurrent PCI in VBD patients necessitates novel approaches.
  • This study focuses on developing a machine learning model for this classification task.

Purpose of the Study:

  • To develop and validate a machine learning-based classification model.
  • To identify concurrent posterior circulation infarction (PCI) in patients with vertebrobasilar dolichoectasia (VBD).
  • To provide a tool for clinical decision-making in VBD patients.

Main Methods:

  • Retrospective data collection from 380 participants.
  • Random forest and XGBoost models were trained and validated.
Keywords:
Basilar arteryClassificationMachine learningPosterior circulation infarctionVertebral arteryVertebrobasilar dolichoectasia

Related Experiment Videos

Last Updated: Jul 12, 2026

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
10:25

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

  • Boruta feature selection and SHAP analysis were employed to identify key predictors.

    • Main Results:

    • The XGBoost model achieved an AUC of 0.884, demonstrating strong performance.
    • Key predictors for concurrent PCI included basilar artery diameter (BAD) and midline distance (BAMD).
    • The model integrated clinical and imaging variables for classification.

    Conclusions:

    • A machine learning model was developed and internally validated to classify concurrent PCI in VBD.
    • External validation and prospective studies are crucial for clinical implementation.
    • A web calculator is available to aid in identifying individuals with concurrent PCI.