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Thoracic, aortic arch and abdominal aneurysms are significant vascular conditions that can present with various clinical manifestations and lead to serious complications. Understanding these manifestations and the appropriate diagnostic studies is essential for effective management and treatment.Thoracic Aortic AneurysmsThoracic aortic aneurysms often remain asymptomatic until they reach a size that impinges on adjacent structures. They typically cause deep, diffuse chest pain that radiates to...

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Related Experiment Video

Updated: Jun 25, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Optimizing Coil Selection for Cerebral Aneurysm Treatment Using PyRadiomics and Machine Learning Models.

Toshiki Koshiba, Soichiro Fujimura, Genki Kudo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study uses machine learning and advanced image analysis to accurately predict the best coil size for treating cerebral aneurysms, improving endovascular coil embolization outcomes.

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    Area of Science:

    • Neurosurgery
    • Medical Imaging
    • Machine Learning

    Background:

    • Cerebral aneurysms pose a significant risk, and endovascular coil embolization is a common treatment.
    • Accurate selection of embolic coils is crucial for successful treatment and preventing complications.
    • Current methods for coil selection can be subjective and rely heavily on manual image interpretation.

    Purpose of the Study:

    • To develop and validate a machine learning-based method for predicting optimal embolic coil dimensions.
    • To enhance the accuracy of initial coil selection for saccular cerebral aneurysms.
    • To reduce subjectivity in the coil selection process for cerebrovascular interventions.

    Main Methods:

    • Analysis of 3D medical images from 273 saccular cerebral aneurysm cases.
    • Comprehensive feature extraction, including morphological and radiological texture data.
    • Development and evaluation of five machine learning regression models using 5-fold cross-validation.

    Main Results:

    • Machine learning models demonstrated high accuracy in predicting optimal coil size and length.
    • Incorporating radiological texture features significantly improved prediction accuracy compared to morphological data alone.
    • The developed method offers a more objective approach to coil selection.

    Conclusions:

    • Advanced image analysis combined with machine learning can significantly improve the accuracy of coil selection for cerebral aneurysm treatment.
    • This approach has the potential to refine treatment strategies and enhance clinical outcomes in cerebrovascular interventions.
    • The study underscores the value of integrating computational methods into neurosurgical planning.