Rezaeitaleshmahalleh M, Lyu Z, Mu Nan, Nainamalai Varatharajan, Tang Jinshan, Gemmete J J, Pandey A S, Jiang J
Department of Biomedical Engineering, Michigan Technological University, H-STEM 339, 1400 Townsend Drive, Houghton, MI, 49931, USA.
Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, 49931, USA.
Ann Biomed Eng. 2025 Apr;53(4):1024-1041. doi: 10.1007/s10439-025-03686-2. Epub 2025 Feb 4.
This study uses a spatial pattern analysis of time-resolved aneurysmal velocity fields to enhance the characterization of intracranial aneurysms' (IA) rupture status. We name this technique temporal velocity-informatics (TVI). In this study, using imaging data obtained from 112 subjects harboring IAs with known rupture status, we reconstructed 3D models to get aneurysmal velocity data by performing computational fluid dynamics (CFD) simulations and morphological information. TVI analyses were conducted for time-resolved velocity fields to quantitatively obtain spatial and temporal flow disturbance. Lastly, we employed four machine learning (ML) methods (e.g., support vector machine [SVM]) to evaluate the prediction performance of the proposed TVI. Overall, the SVM's prediction with TVI performed the best: an area under the curve (AUC) value of 0.92 and a total accuracy of 86%. With TVI, the SVM classifier correctly identified 77 and 92% of ruptured and unruptured IAs, respectively.
本研究采用时间分辨动脉瘤速度场的空间模式分析，以增强对颅内动脉瘤（IA）破裂状态的特征描述。我们将此技术命名为时间速度信息学（TVI）。在本研究中，利用从112名已知破裂状态的IA患者获得的成像数据，我们通过进行计算流体动力学（CFD）模拟和形态学信息重建3D模型，以获取动脉瘤速度数据。对时间分辨速度场进行TVI分析，以定量获得空间和时间流动干扰。最后，我们采用四种机器学习（ML）方法（例如支持向量机[SVM]）来评估所提出的TVI的预测性能。总体而言，TVI与SVM的预测表现最佳：曲线下面积（AUC）值为0.92，总准确率为86%。使用TVI，SVM分类器分别正确识别了77%的破裂IA和92%的未破裂IA。
