ABSTRACT
This scoping review aims to identify and summarize the current literature on Machine learning (ML) approaches for detecting coronary artery disease (CAD) using angiography imaging. We comprehensively searched several databases and identified 23 studies that met the inclusion criteria. They employed different types of angiography imaging including computed tomography and invasive coronary angiography. Several studies have used deep learning algorithms for image classification and segmentation, and our findings show that various machine learning algorithms, such as convolutional neural networks, different types of U-Net, and hybrid approaches. Studies also varied in the outcomes measured, identifying stenosis, and assessing the severity of CAD. ML approaches can improve the accuracy and efficiency of CAD detection by using angiography. The performance of the algorithms differed depending on the dataset used, algorithm employed, and features selected for analysis. Therefore, there is a need to develop ML tools that can be easily integrated into clinical practice to aid in the diagnosis and management of CAD.
Subject(s)
Coronary Artery Disease , Humans , Coronary Artery Disease/diagnostic imaging , Angiography , Algorithms , Databases, Factual , Machine LearningABSTRACT
UNLABELLED: Chronic total occlusion (CTO) intervention is still a challenging problem. The aim of this study is to determine factors that affect PCI results. METHOD AND RESULTS: The study was conducted on 72 patients in two centers. CTO angioplasty was done by the antegrade approach from the femoral and/or radial approach. The role of age, gender, anatomical variations such as calcification, length of the lesion, proximal bending, retrograde filling and occluded coronary artery (LAD, CCK or RCA), and wires were assessed. The success rate was 79.6%, and presence of calcification was an important factor in CTO PCI. Operator's experience, use of appropriate equipment and calcification are important factors in predicting a successful PCI.
