Accurate Diagnosis Of Coronary Artery Disease Research Articles

Performance of the left ventricular myocardium in patients with a negative test result during exercise stress echocardiography

Aim. To determine the possibility of identifying patients with significant coronary artery disease (CAD) by evaluation of the left ventricular (LV) myocardial work indicators by constructing pressure-strain loops despite a negative test result during exercise stress echocardiography. Materials and methods. The study included 79 patients with suspected or previously confirmed CAD, of which 47 (59%) men, who had a negative test result during exercise stress echocardiography on the treadmill. Global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE) were evaluated using the technique of constructing pressure-strain loops at rest and at peak exercise. Results. With similar systolic blood pressure and LV ejection fraction at peak exercise between the control group and the group of patients with single-vessel CAD, there was revealed significant difference in GWI, GCW and GWE at peak exercise. In patients with multi-vessel CAD, GWI and GCW also significantly differed from the control group at peak exercise. Conclusion. The evaluation of LV myocardial work indicators may be recommended for a more accurate diagnosis of CAD in negative or unreliable test results during exercise stress echocardiography.

Visualization of Borderline Coronary Artery Lesions by CT Angiography and Coronary Artery Disease Reporting and Data System

Coronary artery disease (CAD) narrows vessel lumens at the sites of atherosclerosis, increasing the risk of myocardial ischemia or infarction. Early and accurate diagnosis of CAD is crucial to significantly improve prognosis and management. CT angiography (CTA) is a noninvasive imaging technique that enables assessment of vascular structure and stenosis with high resolution and contrast. Coronary CTA is useful in the diagnosis of CAD. Recently, the CAD-reporting and data system (CAD-RADS), a diagnostic classification system based on coronary CTA, has been developed to improve intervention efficacy in patients suspected of CAD. While the CAD-RAD is based on CTA, it includes borderline categories where interpreting the coronary artery status solely based on CTA findings may be challenging. This review introduces CTA findings that fall within the CAD-RADS categories that necessitate additional tests to decide to perform invasive coronary angiography and discusses appropriate management strategies.

A Prediction Model Created by Comparing Tree-Based Methods To Assess Coronary Artery Disease

Aim: As people's quality of life and habits have changed, Coronary Artery Disease (CAD) has become the leading cause of death globally. It is a complicated cardiac disease with various risk factors and a wide range of symptoms. An early and accurate diagnosis of CAD allows for the quick administration of appropriate treatment, which contributes to a decreased mortality rate. Machine learning (ML) algorithms for CAD prediction and treatment decisions are quickly being developed and implemented in clinical practice. Predictive models based on machine learning algorithms may aid health personnel in the early diagnosis of CAD, lowering mortality. Thus, this study goal is to forecast the elements that may be connected with CAD using tree-based approaches, which are one of the machine learning methods, and to discover which factor is more effective on CAD. Material and Methods: The open-access heart disease dataset was used within the scope of the study to investigate the risk factors related with CAD. The data set used contains the values of 333 patients, as well as 20 input and 1 target variables. The 10-fold cross validation approach was employed in the modeling, and the data set was divided as 80%: 20% as training and test datasets. For model assessment, the measures of accuracy (ACC), balanced accuracy (b-ACC), sensitivity (SE), specificity (SP), positive predictive value (ppv), negative predictive value (npv), and F1-score were utilized. Results: The values of ACC, b-ACC, SE, SP, ppv, npv, and F1-score performance metrics were 9 98.5%, 98.8%, 97.7%, 100%, 100%, 95.8% and 98.8%, respectively, as a consequence of the estimate model results created with the XGBoost approach, which has the best performance among tree-based models. When the groups with or without CAD were compared, a statistically significant difference was found in terms of the age variable. There is also a significant relationship between the active, lifestyle, ihd, dm, ecgpatt, qwave variables and the presence/absence of the CAD variable. When the variable significance values obtained as a result of modeling with the highest performing XGBoost are examined, it is seen that the variables that most associated with CAD are ekgpatt: normal, ekgpatt: ST-depression, ekgpatt: T-inversion, qwave: yes, age, bpdias, height, LDL, HR, IVSD: with LVH, bpsyDM. Conclusion: According to the performance criteria of the forecasting models used, CAD gave distinctively successful results in forecasting. By identifying risk factors associated with CAD, the proposed machine learning models can provide clinicians with practical, cost-effective and beneficial assistance in making accurate predictive decisions.

Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm

Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.

Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort. Automatic CAD diagnosis consists of two subtasks. One is a segmentation task, which aims to extract the region of interest (ROI) from original images with U-Net. The second task is an identification task, which we implemented using 3DNet. The coronary artery tree images and clinical parameters were input into 3DNet, and the CAD diagnosis result was output. We built a coronary artery segmentation model based on CCTA images with the corresponding labelling. The segmentation model had a mean Dice value of 0.771 ± 0.021. Based on this model, we built an automated diagnosis model (classification model) for CAD. The average accuracy and area under the receiver operating characteristic curve (AUC) were 0.750 ± 0.056 and 0.737, respectively. Herein, using a deep learning algorithm, we realized the rapid classification and diagnosis of CAD from CCTA images in two steps. Our deep learning model can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis. Artificial intelligence methods such as deep learning have the potential to elevate the efficiency in CCTA image analysis considerably. • The deep learning model rapidly achieved a high Dice value (0.771 ± 0.0210) in the autosegmentation of coronary arteries using CCTA images. • Based on the segmentation model, we built a CAD autoclassifier with the 3DNet algorithm, which achieved a good diagnostic performance (AUC) of 0.737. • The deep neural network could be used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.

P6435A diagnostic prediction model of coronary artery disease in patient with chest pain using machine learning

Abstract Background Chest pain is a major symptom of coronary artery disease (CAD), which can lead to acute coronary syndrome and sudden cardiac death. Accurate diagnosis of CAD in patients who experience chest pain is crucial to provide appropriate treatment and optimize clinical outcomes. Objective This study was to develop a machine learning model which can predict and diagnose CAD in patients complaining of chest pain based on a large real-world prospective registry database and computing power. Method A total of 10,177 subjects with typical or atypical chest pain who underwent a coronary angiography at the cardiovascular center of our University Hospital, South Korea between November 2004 and May 2014 were evaluated in this study. The generation of the diagnostic prediction model for CAD used the classification application by technical support of MATLAB R2017a. The performance evaluation of the learning model generated by machine learning was evaluated by the area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis. Results The diagnostic prediction model of CAD had been generated according to the user's accessibility such as the general public or clinician (Model 1–4). The performance of the models has ranged from 0.78 to 0.96 by the AUC of ROC analysis. The prediction accuracy of the models ranged from 70.4% to 88.9%. The performance of the diagnostic prediction model of CAD by machine learning improved as the input information increased. Figure 1. Study Flow Chart Conclusion A diagnostic prediction model of CAD using the machine learning method and the registry database was developed. Further studies are needed to verify our results.

The clinical role of stress myocardial perfusion imaging in women with suspected coronary artery disease.

Cardiovascular disease remains the number one cause of mortality for women in the United States, with coronary artery disease (CAD) accounting for 54% of all cardiovascular deaths. CAD claims the lives of more than 250,000 women each year and is therefore the single largest killer of American women. For several decades, the under-representation of women in clinical trials led to both a lack of available sex-specific evidence and a generalized misconception that CAD was a "man's disease." In actuality, not only are women vulnerable to CAD, they typically develop it 10 to 15 years later than men. Furthermore, sex differences exist in the mortality rates of women and men with CAD, such that once CAD is present in women, they have worse outcomes than their male counterparts. Consequently, early and accurate diagnosis of CAD is crucial for reducing mortality rates in women. Stress myocardial perfusion imaging (MPI) using contemporary techniques has been shown to have significant value in the diagnosis and prognosis of CAD in women. In the risk assessment of women with an intermediate clinical pretest likelihood of CAD, using MPI with exercise or pharmacologic stress has been shown to add incremental value to clinical variables or exercise electrocardiogram stress testing alone. This review discusses the clinical role of stress MPI in the management of women with suspected CAD.