The Rsearch Center of Intelligent Medical Information Processing, School of Information Science and Engineering, Shandong University, Qingdao 266237, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China.
Department of Nephrology, Qilu Hospital of Shandong University, No.107 Wenhuaxi Road, Jinan 250012, China.
Comput Med Imaging Graph. 2020 Apr;81:101711. doi: 10.1016/j.compmedimag.2020.101711. Epub 2020 Feb 19.
Plaque rupture and subsequent thrombosis are major processes of acute cardiovascular events. The Vulnerability Index is a very important indicator of whether a plaque is ruptured, and these easily ruptured or fragile plaques can be detected early. The higher the general vulnerability index, the higher the instability of the plaque. Therefore, determining a clear vulnerability index classification point can effectively reduce unnecessary interventional therapy. However, the current critical value of the vulnerability index has not been well defined. In this study, we proposed a neural network-based method to determine the critical point of vulnerability index that distinguishes vulnerable plaques from stable ones. Firstly, based on MatConvNet, the intravascular ultrasound images under different vulnerability index labels are classified. Different vulnerability indexes can obtain different accuracy rates for the demarcation points. The corresponding data points are fitted to find the existing relationship to judge the highest classification. In this way, the vulnerability index corresponding to the highest classification accuracy rate is judged. Then the article is based on the same experiment of different components of the aortic artery in the artificial neural network, and finally the vulnerability index corresponding to the highest classification accuracy can be obtained. The results show that the best vulnerability index point is 1.716 when the experiment is based on the intravascular ultrasound image, and the best vulnerability index point is 1.607 when the experiment is based on the aortic artery component data. Moreover, the vulnerability index and classification accuracy rate has a periodic relationship within a certain range, and finally the highest AUC is 0.7143 based on the obtained vulnerability index point on the verification set. In this paper, the convolution neural network is used to find the best vulnerability index classification points. The experimental results show that this method has the guiding significance for the classification and diagnosis of vulnerable plaques, further reduce interventional treatment of cardiovascular disease.
斑块破裂和随后的血栓形成是急性心血管事件的主要过程。易损指数是斑块是否破裂的一个非常重要的指标，这些容易破裂或脆弱的斑块可以被早期检测到。一般来说，易损指数越高，斑块的不稳定性就越高。因此，确定一个明确的易损指数分类点可以有效地减少不必要的介入治疗。然而，目前易损指数的临界值还没有得到很好的定义。在本研究中，我们提出了一种基于神经网络的方法，用于确定易损指数的临界点，以区分易损斑块和稳定斑块。首先，基于 MatConvNet，对不同易损指数标签下的血管内超声图像进行分类。不同的易损指数可以为划分点获得不同的准确率。拟合相应的数据点以找到现有的关系来判断最高的分类。这样，就可以判断出对应于最高分类准确率的易损指数。然后，文章基于人工神经网络对不同主动脉成分的相同实验，最终可以得到对应于最高分类准确率的易损指数。结果表明，基于血管内超声图像的实验最佳易损指数点为 1.716，基于主动脉成分数据的实验最佳易损指数点为 1.607。此外，易损指数和分类准确率在一定范围内存在周期性关系，最终在验证集上基于获得的易损指数点获得的 AUC 值最高可达 0.7143。本文使用卷积神经网络来寻找最佳易损指数分类点。实验结果表明，该方法对易损斑块的分类和诊断具有指导意义，进一步减少了心血管疾病的介入治疗。
