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Related Experiment Video

Updated: Sep 14, 2025

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A Dynamic Machine Learning Model to Predict Angiographic Vasospasm After Aneurysmal Subarachnoid Hemorrhage.

Rajeev D Sen1, Margaret C McGrath2, Varadaraya S Shenoy2

  • 1Department of Neurological Surgery, New York University Langone Medical Center, New York , New York , USA.

Neurosurgery
|July 24, 2025
PubMed
Summary

A new machine learning model using daily transcranial Doppler ultrasound (TCD) data accurately predicts angiographic vasospasm (AV) after aneurysmal subarachnoid hemorrhage (aSAH). This dynamic approach offers higher precision than static TCD measurements for early detection and intervention.

Keywords:
AneurysmCerebral vasospasmMachine learningPrediction modelSubarachnoid hemorrhageTranscranial Doppler

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Aneurysmal subarachnoid hemorrhage (aSAH) poses significant risks, including the development of angiographic vasospasm (AV).
  • Early and accurate prediction of AV is crucial for timely intervention and improved patient outcomes.
  • Current prediction methods may lack the precision and dynamism required for complex clinical scenarios.

Purpose of the Study:

  • To develop a precise, dynamic machine learning model for predicting AV in aSAH patients.
  • To leverage daily transcranial Doppler ultrasound (TCD) data for enhanced predictive capabilities.
  • To compare the performance of a dynamic LSTM model against static models.

Main Methods:

  • Retrospective review of 424 aSAH patients.
  • Collection of demographic, clinical, radiographic, and quantitative data (e.g., MAP, CPP, serum sodium, TCD parameters).
  • Development and comparison of three models: Baseline Model (BM), Standard TCD Model (SM), and Long Short-Term Memory (LSTM) machine learning model.

Main Results:

  • The LSTM model demonstrated the highest precision (0.571) and accuracy (0.776) in predicting AV.
  • The LSTM model maintained high precision (0.488) and accuracy (0.803) for predicting AV within 5 days.
  • Removing non-TCD data improved LSTM model precision to 0.824, highlighting the value of TCD data.

Conclusions:

  • Longitudinal TCD data can power dynamic machine learning models for AV prediction.
  • The developed LSTM model offers superior precision compared to static TCD measurements.
  • This dynamic approach holds promise for improving AV prediction in aSAH management.