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Related Experiment Video

Updated: May 24, 2026

Analysis of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage with High Frequency Transcranial Duplex Ultrasound
10:41

Analysis of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage with High Frequency Transcranial Duplex Ultrasound

Published on: June 3, 2021

OPTIMA-DAW: Improving Cerebral Vasospasm Detection After Aneurysmal Subarachnoid Haemorrhage Using Machine Learning.

Claire Charamel1,2, Arthur Le Gall2, Marc Cuggia1

  • 1Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, Rennes, France.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

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Machine learning models can predict cerebral vasospasm after aneurysmal subarachnoid hemorrhage (aSAH). XGBoost demonstrated strong performance in identifying this serious complication using clinical data.

Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Cerebral vasospasm is a significant risk following aneurysmal subarachnoid hemorrhage (aSAH).
  • Early detection and prediction of cerebral vasospasm are crucial for patient management.
  • Standardized clinical data formats facilitate large-scale analysis.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting cerebral vasospasm post-aSAH.
  • To assess the performance of different machine learning algorithms using real-world clinical data.

Main Methods:

  • Trained machine learning models on a dataset of 168 patients with aneurysmal subarachnoid hemorrhage.
  • Utilized 225 computed tomography angiography (CTA) timepoints for model training.
Keywords:
Cerebral vasospasm (CV)Clinical Decision Support SystemMachine Learning (ML)OMOP Common Data Model (OMOP CDM)

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A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
08:12

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

Related Experiment Videos

Last Updated: May 24, 2026

Analysis of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage with High Frequency Transcranial Duplex Ultrasound
10:41

Analysis of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage with High Frequency Transcranial Duplex Ultrasound

Published on: June 3, 2021

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage
08:12

A Volumetric Method for Quantification of Cerebral Vasospasm in a Murine Model of Subarachnoid Hemorrhage

Published on: July 28, 2018

  • Employed the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardized clinical data.

    • Main Results:

    • The XGBoost model achieved the highest predictive performance.
    • The area under the receiver operating characteristic curve (AUROC) for XGBoost was 0.79 (95% CI 0.65-0.91).

    Conclusions:

    • Machine learning, particularly XGBoost, shows promise in predicting cerebral vasospasm after aSAH.
    • Standardized clinical data and machine learning can aid in identifying patients at risk for this complication.