Escuela de Data Science, Facultad de Estudios Interdisciplinarios, Universidad Mayor, Santiago, Chile.
Departamento de Ingeniera Mecánica, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Santiago, Chile.
Proc Inst Mech Eng H. 2021 Jun;235(6):655-662. doi: 10.1177/09544119211000477. Epub 2021 Mar 8.
Morphological characterization and fluid dynamics simulations were carried out to classify the rupture status of 71 (36 unruptured, 35 ruptured) patient specific cerebral aneurysms using a machine learning approach together with statistical techniques. Eleven morphological and six hemodynamic parameters were evaluated individually and collectively for significance as rupture status predictors. The performance of each parameter was inspected using hypothesis testing, accuracy, confusion matrix, and the area under the receiver operating characteristic curve. Overall, the size ratio exhibited the best performance, followed by the diastolic wall shear stress, and systolic wall shear stress. The prediction capability of all 17 parameters together was evaluated using eight different machine learning algorithms. The logistic regression achieved the highest accuracy (0.75), whereas the random forest had the highest area under curve value among all the classifiers (0.82), surpassing the performance exhibited by the size ratio. Hence, we propose the random forest model as a tool that can help improve the rupture status prediction of cerebral aneurysms.
采用机器学习方法结合统计技术，对 71 个（36 个未破裂，35 个破裂）患者特定脑动脉瘤进行形态学特征分析和流体动力学模拟，以对其破裂状态进行分类。分别评估了 11 个形态学和 6 个血流动力学参数作为破裂状态预测因子的重要性。使用假设检验、准确性、混淆矩阵和接收者操作特征曲线下面积来检查每个参数的性能。总的来说，大小比表现出最好的性能，其次是舒张期壁切应力和收缩期壁切应力。使用八种不同的机器学习算法评估了所有 17 个参数的综合预测能力。逻辑回归的准确率最高（0.75），而随机森林在所有分类器中的曲线下面积最高（0.82），超过了大小比的表现。因此，我们提出随机森林模型作为一种工具，可以帮助提高脑动脉瘤破裂状态的预测。
