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整合基于PointNet的模型和机器学习算法来分类IA的断裂状态

Yilu Shou¹, Zhenpeng Chen¹, Pujie Feng¹

¹School of Biomedical Engineering, Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, No. 10, Xitoutiao, Youanmenwai, Fengtai District, Beijing 100069, China.

Bioengineering (Basel, Switzerland)

|July 27, 2024

概括

预测内动脉瘤 (IA) 破裂风险是一项挑战. 将PointNet和机器学习与血液动力学云功能集成,显著提高了IA破裂状态分类准确性和AUC.

科学领域:

神经外科神经外科
医疗成像医学成像
计算流体动力学的流体动力学.

背景情况:

内动脉瘤 (IAs) 破裂导致脑下关节下出血,导致高死亡率和残疾.
准确预测IA破裂风险仍然是一个重大的临床挑战.

研究的目的:

开发和评估一种有效的方法来分类IA断裂状态.
将基于PointNet的模型与机器学习算法集成在一起,以改善预测.

主要方法:

从数字减去血管学 (DSA) 数据构建了IA的3D几何模型.
血液动力学参数和"血液动力学云"是使用计算流体动力学 (CFD) 计算的.
一个PointNet模型从血液动力云中提取了特征,然后使用机器学习算法进行分类.

主要成果:

最好的分类性能是通过使用16维的血液动力学云特征与几何和血液动力学参数相结合来实现的.
结合这些特征的机器学习模型的准确度高达0.908,AUC高达0.946.
这些特征显著优于仅使用几何和血液动力学参数的模型.

结论:

PointNet和机器学习的整合有效地对IA断裂状态进行了分类.
血液动力学云特征对预测IA破裂作出了重大贡献.
开发的模型为IA的临床诊断和治疗提供了宝贵的见解.

关键词:

这是一个点网点网点网点网点网点网点网点网点网点网点网点网点网点网点网.几何参数的几何参数血液动力学云血液动力学云血液动力学参数血液动力学参数内动脉瘤内动脉瘤机器学习是机器学习.破裂的风险破裂的风险

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Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,