中国医药科学2025，Vol.15Issue(19)：20-23,4.DOI:10.20116/j.issn2095-0616.2025.19.04
基于多维特征的颅内动脉瘤风险预测与模型评估
Risk prediction and model evaluation of intracranial aneurysms based on multidimensional features
摘要
Abstract
Objective To accurately predict the risk of intracranial aneurysm rupture,facilitating early diagnosis and personalized treatment for patients.Methods By integrating morphological features(e.g.,maximum diameter,neck width,lobulation,volume)and hemodynamic characteristics(e.g.,wall shear stress,oscillatory shear index,pressure distribution)of aneurysms,feature data were extracted using Mimics and Ansys-Fluent software.A risk prediction model was developed based on the PyTorch framework,incorporating both traditional machine learning algorithms and deep learning models for intracranial aneurysm rupture risk prediction.Results The study demonstrated that machine learning models constructed using parallel tree boosting algorithms and random forests outperformed deep learning methods(represented by multilayer perceptrons)in predicting rupture risks,proving more suitable for intracranial aneurysm risk assessment.Conclusion The proposed aneurysm rupture risk prediction model can assist physicians in formulating more precise diagnostic and therapeutic plans,thereby improving treatment efficacy and patient safety.关键词
机器学习/医学图像处理/医学图像分割/深度学习模型Key words
Machine learning/Medical image processing/Medical image segmentation/Deep learning model分类
医药卫生引用本文复制引用
李永生,陈广新,董祥梅,徐缘缘,张思瑾,吕春会..基于多维特征的颅内动脉瘤风险预测与模型评估[J].中国医药科学,2025,15(19):20-23,4.基金项目
黑龙江省卫生健康委员会科研项目(20230909010383) （20230909010383）
黑龙江省省属高等学校基本科研业务费科研项目(2023-KYYWF-0943). （2023-KYYWF-0943）
