College of Medical Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China.
School of Sports and Health, Shanghai University of Sport, Shanghai, China.
Biomed Eng Online. 2024 Sep 12;23(1):95. doi: 10.1186/s12938-024-01286-0.
Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The anomalous origin of the coronary artery may lead to problems such as narrowing of the coronary arteries at the beginning of the coronary arteries and abnormal alignment, which may lead to myocardial ischemia due to the compression of the coronary arteries. Clinical symptoms include chest tightness and dyspnea, with angina pectoris as a common symptom that can be life-threatening. Timely and accurate diagnosis of anomalous coronary artery origin is of great importance. Coronary computed tomography angiography (CCTA) can provide detailed information on the characteristics of coronary arteries. Therefore, we combined CCTA and artificial intelligence (AI) technology to analyze the CCTA image features and clinical features of patients with anomalous origin of the right coronary artery to predict angina pectoris and the relevance of different features to angina pectoris.
In this retrospective analysis, we compiled data on 15 characteristics from 126 patients diagnosed with anomalous right coronary artery origins. The dataset encompassed both CCTA imaging attributes, such as the positioning of the right coronary artery orifices and the alignment of coronary arteries, and clinical parameters including gender and age. To identify the most salient features, we employed the Chi-square feature selection method, which filters features based on their statistical significance. We then focused on features yielding a Chi-square score exceeding a threshold of 1, thereby narrowing down the selection to seven key variables, including cardiac function and gender. Subsequently, we evaluated seven classifiers known for their efficacy in classification tasks. Through rigorous training and testing, we conducted a comparative analysis to identify the top three classifiers with the highest accuracy rates.
The top three classifiers in this study are Support Vector Machine (SVM), Ensemble Learning (EL), and Kernel Approximation Classifier. Among the SVM, EL and Kernel Approximation Classifier-based classifiers, the best performance is achieved for linear SVM, optimizable Ensembles Learning and SVM kernel, respectively. And the corresponding accuracy is 75.7%, 75.7%, and 73.0%, respectively. The AUC values are 0.77, 0.80, and 0.75, respectively.
Machine learning (ML) models can predict angina pectoris caused by the origin anomalous of the right coronary artery, providing valuable auxiliary diagnostic information for clinicians and serving as a warning to clinicians. It is hoped that timely intervention and treatment can be realized to avoid serious consequences such as myocardial infarction.
冠状动脉异常起源是一种常见的冠状动脉解剖异常。冠状动脉的异常起源可能导致冠状动脉起始处狭窄和异常排列等问题，这可能导致冠状动脉受压引起心肌缺血。临床症状包括胸闷和呼吸困难，以心绞痛为常见症状，可能危及生命。及时准确地诊断冠状动脉异常起源非常重要。冠状动脉计算机断层血管造影（CCTA）可以提供冠状动脉特征的详细信息。因此，我们结合 CCTA 和人工智能（AI）技术来分析右冠状动脉异常起源患者的 CCTA 图像特征和临床特征，以预测心绞痛和不同特征与心绞痛的相关性。
在这项回顾性分析中，我们从 126 例被诊断为右冠状动脉异常起源的患者中编制了 15 个特征的数据。数据集包括 CCTA 成像属性，如右冠状动脉口的定位和冠状动脉的排列，以及临床参数，如性别和年龄。为了识别最显著的特征，我们采用了卡方特征选择方法，该方法根据特征的统计学意义进行特征过滤。然后，我们专注于卡方得分超过阈值 1 的特征，从而将选择范围缩小到七个关键变量，包括心功能和性别。随后，我们评估了七种在分类任务中表现出色的分类器。通过严格的训练和测试，我们进行了比较分析，以确定准确率最高的前三名分类器。
本研究中的前三个分类器是支持向量机（SVM）、集成学习（EL）和核逼近分类器。在 SVM、EL 和核逼近分类器的分类器中，线性 SVM、可优化的集成学习和 SVM 核的性能最好，其准确率分别为 75.7%、75.7%和 73.0%。AUC 值分别为 0.77、0.80 和 0.75。
机器学习（ML）模型可以预测右冠状动脉异常起源引起的心绞痛，为临床医生提供有价值的辅助诊断信息，并向临床医生发出警告。希望能够及时进行干预和治疗，避免心肌梗死等严重后果。
