RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights.

Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the automatic screening of CMR images for triage.

RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance.

Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive testing.

FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model.

Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography.

CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering.

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age.

Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID-19.

Introduction: To reduce mortality in hospitalized patients with COVID-19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID-19.

Methods: This study was conducted on 660 hospitalized patients with CVD and COVID-19 recruited between January 2020 and January 2021 in Iran.

Objective evaluation of deep uncertainty predictions for COVID-19 detection.

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence.

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets.

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread.

Arrhythmogenic right ventricular cardiomyopathy in patients with biallelic JUP-associated skin fragility.

Arrhythmogenic right ventricular cardiomyopathy (ARVC), with skin manifestations, has been associated with mutations in JUP encoding plakoglobin. Genotype-phenotype correlations regarding the penetrance of cardiac involvement, and age of onset have not been well established. We examined a cohort of 362 families with skin fragility to screen for genetic mutations with next-generation sequencing-based methods.

Value of pathologic Q wave in surface electrocardiography in the prediction of myocardial nonviability: A cardiac magnetic resonance imaging-based study.

In surface electrocardiography (ECG), Q wave is often considered as a sign of irreversibly scarred myocardium. Cardiac magnetic resonance (CMR) imaging is an accurate mean for the detection of myocardial viability. Herein, we study the predictive value of Q wave in nonviable (scarred) myocardium by CMR study.

Quantification of Left Ventricle Function in Stress Echocardiography Using Endocardial Area Centroid Trajectory.

Coronary artery occlusion has a direct effect on cardiac activity and is a well-known reason for ventricular motion disorder, specifically left ventricle (LV) wall motion dysfunction. In stress echocardiography, wall motion abnormality is exaggerated when stress is applied to the heart, and therefore, it is easier to diagnose abnormality in ventricular motion. In this study, we have presented a new parameter that measures LV function.

Congenital partial absence of the pericardium presenting with a rare concurrent abnormality of vascular ring diagnosed by cardiac magnetic resonance imaging.

Congenital absence of the pericardium is a rare abnormality that can be diagnosed by cardiac imaging procedures. A 49-year-old male needed medical attention due to the appearance of palpitation with a systolic murmur, and a notable aortic arch deviation was seen in the chest X-ray. In the echocardiogram, a poor echo window was detected.

Temporal Super Resolution Enhancement of Echocardiographic Images Based on Sparse Representation.

A challenging issue for echocardiographic image interpretation is the accurate analysis of small transient motions of myocardium and valves during real-time visualization. A higher frame rate video may reduce this difficulty, and temporal super resolution (TSR) is useful for illustrating the fast-moving structures. In this paper, we introduce a novel framework that optimizes TSR enhancement of echocardiographic images by utilizing temporal information and sparse representation.

Doppler-derived strain and strain rate imaging assessment of right ventricular systolic function in adults late after tetralogy of Fallot repair: an observational study.

Objective: Tetralogy of Fallot (TOF) is the most common form of cyanotic congenital heart disease. Today, we are faced with an increasing number of patients with residual pulmonary regurgitation (PR) late after TOF repair. The right ventricular (RV) volumes and function are among the most important factors influencing clinical decision-making.

Diagnosis of coronary arteries stenosis using data mining.

Cardiovascular diseases are one of the most common diseases that cause a large number of deaths each year. Coronary Artery Disease (CAD) is the most common type of these diseases worldwide and is the main reason of heart attacks. Thus early diagnosis of CAD is very essential and is an important field of medical studies.

Modified optical flow technique for cardiac motions analysis in echocardiography images.

The quantitative analysis of cardiac motions in echocardiography images is a noteworthy issue in processing of these images. Cardiac motions can be estimated by optical flow (OF) computation in different regions of image which is based on the assumption that the intensity of a moving pattern remains constant in consecutive frames. However, in echocardiographic sequences, this assumption may be violated because of unique specifications of ultrasound.

A data mining approach for diagnosis of coronary artery disease.

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly.

Effect of exercise on blood flow through the aortic valve: a combined clinical and numerical study.

The aim of this study was to measure the cardiac output and stroke volume for a healthy subject by coupling an echocardiogram Doppler (echo-Doppler) method with a fluid-structure interaction (FSI) simulation at rest and during exercise. Blood flow through aortic valve was measured by Doppler flow echocardiography. Aortic valve geometry was calculated by echocardiographic imaging.

A New Method for Pseudo-increasing Frame Rates of Echocardiography Images Using Manifold Learning.

Increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. In this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimensional manifold spaces. In this method, we presume that the dimensionality of echocardiography images obtained from a patient is artificially high and the images can be described as functions of only a few underlying parameters such as periodic motion due to heartbeat.

Automatic detection of end-diastole and end-systole from echocardiography images using manifold learning.

The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the frames of one cycle of heart motion. By this approach the nonlinear embedded information in sequential images is represented in a two-dimensional manifold by the LLE algorithm and each image is depicted by a point on reconstructed manifold.