CNNFM Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, 1450 Kargar St. N., Tehran, 14399-57131, Iran.
STRETCH Lab, Department of Biomedical Engineering and Mechanics, Virginia Tech, 330A Kelly Hall, 325 Stanger Street, Blacksburg, VA, 24061, USA.
Sci Rep. 2024 Jul 9;14(1):15777. doi: 10.1038/s41598-024-66840-1.
Cerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatment becomes evident upon diagnosis, as the severity of the disease guides the course of action. Cerebral aneurysms are particularly vulnerable in the circle of Willis and pose a significant concern due to the potential for rupture, which can lead to irreversible consequences, including fatality. The primary objective of this study is to predict the rupture status of cerebral aneurysms. To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. Diagnosing and predicting aneurysm rupture based solely on brain scans is a significant challenge with limited reliability, even for experienced physicians. However, by employing statistical methods and machine learning techniques, we can assist physicians in making more confident predictions regarding rupture likelihood and selecting appropriate treatment strategies. To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. The dataset comprised 3 clinical features and 35 morphological parameters, including 8 novel morphological features introduced for the first time in this study. Our models demonstrated exceptional performance in predicting cerebral aneurysm rupture, with accuracy ranging from 0.76 to 0.82 and precision score from 0.79 to 0.83 for the test dataset. As the data are sensitive and the condition is critical, recall is prioritized as the more crucial parameter over accuracy and precision, and our models achieved outstanding recall score ranging from 0.85 to 0.92. Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. The ellipticity index, size ratio, and shape irregularity are pivotal features in predicting aneurysm rupture, respectively, contributing significantly to our understanding of this complex condition. Among the multitude of parameters under investigation, these are particularly important. In this study, the ideal roundness parameter was introduced as a novel consideration and ranked fifth among all 38 parameters. Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.
脑动脉瘤是一种沉默但普遍存在的疾病，影响着全球相当一部分人群。其形成原因众多，临床表现和治疗方式各异。在诊断后，选择合适的治疗方案至关重要，因为疾病的严重程度决定了治疗的方向。脑动脉瘤在 Willis 环中尤为脆弱，且存在破裂的风险，可能导致无法挽回的后果，甚至死亡。本研究的主要目的是预测脑动脉瘤的破裂状态。为实现这一目标，我们利用了一个综合数据集，其中包含了从以前患者的 3D 真实几何形状中提取的临床和形态学数据。本研究旨在提供有价值的见解，以帮助在治疗过程中做出明智的决策，并可能拯救未来患者的生命。仅依靠脑部扫描来诊断和预测动脉瘤破裂具有很大的挑战性，即使对于经验丰富的医生来说也是如此。然而，通过运用统计方法和机器学习技术，我们可以帮助医生更自信地预测破裂的可能性，并选择合适的治疗策略。为此，我们使用了 5 种分类机器学习算法，并在一个包含 708 个脑动脉瘤的大型数据库上对其进行了训练。该数据集包含 3 个临床特征和 35 个形态学参数，其中包括 8 个在本研究中首次引入的新形态学特征。我们的模型在预测脑动脉瘤破裂方面表现出色，测试数据集的准确率为 0.76 到 0.82，精确率为 0.79 到 0.83。由于数据敏感且情况危急，召回率优先于准确率和精确率，我们的模型实现了出色的召回率，范围为 0.85 到 0.92。总体而言，最佳模型是支持向量机，在测试数据集上的准确率和精确率为 0.82，召回率为 0.92，曲线下面积为 0.84。椭圆度指数、大小比和形状不规则性是预测动脉瘤破裂的关键特征，分别对我们理解这种复杂情况有重要贡献。在研究的众多参数中，这些特征尤为重要。在本研究中，引入了理想的圆形度参数，并在所有 38 个参数中排名第五。新参数中的颈部周长和出口数量也被认为是重要的贡献因素。
