Machine Learning Prediction of Aneurysm Rupture Risk Based on the Fusion of Hemodynamic and Morphological Features

Chen Guangxin; Cai Ying; Guo Jinxing; Yu Chenglong

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Machine Learning Prediction of Aneurysm Rupture Risk Based on the Fusion of Hemodynamic and Morphological Features

更新时间：2024-05-22

- Machine Learning Prediction of Aneurysm Rupture Risk Based on the Fusion of Hemodynamic and Morphological Features
- New Generation of Information Technology Pages: 1-7(2024)
- 作者机构：
  
  1.牡丹江医学院医学影像学院，黑龙江牡丹江，157011
  2.牡丹江医学院附属红旗医院，黑龙江牡丹江，157011
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP30
- Published Online：22 May 2024，
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陈广新,才莹,郭金兴等.基于血流动力学与形态学特征融合的动脉瘤破裂风险机器学习预测研究[J].新一代信息技术,

Chen Guangxin,Cai Ying,Guo Jinxing,et al.Machine Learning Prediction of Aneurysm Rupture Risk Based on the Fusion of Hemodynamic and Morphological Features[J].New Generation of Information Technology,
陈广新,才莹,郭金兴等.基于血流动力学与形态学特征融合的动脉瘤破裂风险机器学习预测研究[J].新一代信息技术, DOI：10.3969/j.issn.2096-6091.XXXX.XX.001.

Chen Guangxin,Cai Ying,Guo Jinxing,et al.Machine Learning Prediction of Aneurysm Rupture Risk Based on the Fusion of Hemodynamic and Morphological Features[J].New Generation of Information Technology, DOI：10.3969/j.issn.2096-6091.XXXX.XX.001.

摘要

目的

本研究旨在通过整合动脉瘤患者的血流动力学与形态学特征，构建一个预测动脉瘤破裂风险的机器学习模型。

方法

收集了2021年2月至2023年12月期间在牡丹江医学院附属红旗医院神经外科就诊的颅内动脉瘤患者的病例。研究组包括130例动脉瘤破裂患者，对照组包括60例未破裂的动脉瘤患者。所有患者均提供了DICOM格式的CT图像，并在获得医院伦理委员会的批准和患者家属签署知情同意书后，使用MIMICS软件对动脉瘤的DICOM格式CT影像进行了三维重建。通过形态学测量，我们获得了形态学参数指标，并通过计算流体力学仿真计算得到了血流动力学参数。将形态学参数与血流动力学参数相结合，构建了一个用于机器学习临床预测模型的数据集。研究采用了10种机器学习算法构建预测模型，并使用准确率、AUC（曲线下面积）、召回率和F1分数等指标进行评估。此外基于SHAP方法进行模型可解释性分析。

结果

在所有测试的模型中，随机森林模型展现出最佳性能，其准确率达到了0.78，AUC值为0.81，且召回率也较高，达到了0.72。

结论

通过融合动脉瘤的形态学特征与血流动力学特征构建的机器学习模型，能够为临床决策提供有力的工具，并展现出良好的临床应用潜力。

Abstract

Objective

The aim of this study is to construct a machine learning model for predicting the risk of aneurysm rupture by integrating hemodynamic and morphological features of patients with intracranial aneurysms.

Methods

Cases of patients with intracranial aneurysms who sought treatment at the Department of Neurosurgery

Affiliated Hongqi Hospital of Mudanjiang Medical University from February 2021 to December 2023 were collected. The study group comprised 130 patients with ruptured aneurysms

while the control group included 60 patients with unruptured aneurysms. All patients provided CT images in DICOM format

and after obtaining approval from the hospital's ethics committee and signed informed consent from the patients' families

three-dimensional reconstruction of the aneurysm DICOM format CT images was performed using MIMICS software. Morphological parameters were obtained through morphological measurements

and hemodynamic parameters were calculated through computational fluid dynamics simulations. The dataset for constructing the machine learning clinical prediction model was built by combining morphological and hemodynamic parameters. Ten machine learning algorithms were employed to construct the prediction models

and their performance was evaluated using metrics such as accuracy

AUC (Area Under the Curve)

recall rate

and F1 score. Additionally

model interpretability was analyzed using the SHAP method.

Results

Among all the models tested

the random forest model demonstrated the best performance with an accuracy of 0.78

an AUC value of 0.81

and a high recall rate of 0.72.

Conclusion

The machine learning model constructed by integrating the morphological and hemodynamic features of aneurysms can provide a powerful tool for clinical decision-making and shows promising potential for clinical application.

关键词

动脉瘤破裂风险血流动力学特征形态学特征机器学习模型可解释性

Keywords

AneurysmRupture RiskHemodynamic FeaturesMorphological FeaturesMachine LearningModel Interpretability

references

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Related Institution

Mudanjiang Medical University Affiliated Hongqi Hospital

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