UTILITY OF THE RATIO BETWEEN THE SIZE OF THE RIGHT ATRIUM AND THE RIGHT VENTRICLE AT END-SYSTOLE TO DIAGNOSE ATRIAL SECONDARY TRICUSPID REGURGITATION.

Background: In the multiparametric framework for diagnosing atrial secondary tricuspid regurgitation (A-STR), an end-systolic (ES) right atrial (RA) to right ventricular (RV) volume or area ratio ≥1.5 supports the diagnosis of A-STR over the ventricular STR phenotype (V-STR). However, this threshold value has never been tested.

Association With Outcomes of Correcting the Proximal Isovelocity Surface Area Method to Quantitate Secondary Tricuspid Regurgitation.

Background: Although the correction of the proximal isovelocity surface area (PISA) method has been shown to improve the accuracy of assessing the severity of secondary tricuspid regurgitation (STR), its clinical impact remains to be investigated. The aim of this study was to compare the association of the quantitative parameters of STR severity obtained from the corrected and conventional PISA methods with outcomes.

Methods: Both conventional and corrected effective regurgitant orifice area (EROA) (EROA vs corrected EROA [EROAc]), regurgitant volume (RegVol) (RegVol vs corrected RegVol [RegVolc]), and regurgitant fraction (RegFr) (RegFr vs corrected RegFr [RegFrc]) were measured in 519 consecutive patients (mean age, 75 ± 12 years; 44% men; 74% with ventricular STR) with moderate and severe STR.

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respect to expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses.

Assessing right atrial size in patients with tricuspid regurgitation: importance of the right ventricular-focused view.

Aims: To assess the accuracy of measuring the right atrial volume (RAV) using two-dimensional echocardiography (2DE) in a right ventricular focused (RVF) view compared to the conventional apical four-chamber (4Ch) view in patients with secondary tricuspid regurgitation (STR). We also compared the clinical correlates of the measures obtained using different methods.

Methods And Results: The accuracy of RAV measurements obtained between 2DE-4Ch and RVF views in 384 patients with STR were compared using three-dimensional echocardiography (3DE) as a reference.

Timing and Patient Position During Cuff Blood Pressure Measurement Affect Myocardial Work Parameters Measured by Echocardiography.

Background: Although cuff blood pressure measurement is a critical parameter to calculate myocardial work noninvasively, there is no recommendation about when and how to measure it. Accordingly, we sought to evaluate the effects of the timing during the echo study and the patient's position on the scanning bed during the cuff blood pressure measurement on myocardial work parameter calculations.

Methods: One hundred one consecutive patients (44 women, 66 ± 14 years) undergoing clinically indicated echocardiography were prospectively enrolled.

Peak atrial longitudinal strain and risk stratification in moderate and severe aortic stenosis.

Aims: We sought to investigate the association of left atrial strain with the outcome in a large cohort of patients with at least moderate aortic stenosis (AS).

Methods And Results: We analysed 467 patients (mean age 80.6 ± 8.

Clinical Impact of the Volumetric Quantification of Ventricular Secondary Mitral Regurgitation by Three-Dimensional Echocardiography.

Background: The assessment of ventricular secondary mitral regurgitation (v-SMR) severity through effective regurgitant orifice area (EROA) and regurgitant volume (RegVol) calculations using the proximal isovelocity surface area (PISA) method and the two-dimensional echocardiography volumetric method (2DEVM) is prone to underestimation. Accordingly, we sought to investigate the accuracy of the three-dimensional echocardiography volumetric method (3DEVM) and its association with outcomes in v-SMR patients.

Methods: We included 229 patients (70 ± 13 years, 74% men) with v-SMR.

Right Atrial Remodeling and Outcome in Patients with Secondary Tricuspid Regurgitation.

Background: In patients with secondary tricuspid regurgitation (STR), right atrial remodeling (RAR) is a proven marker of disease progression. However, the prognostic value of RAR, assessed by indexed right atrial volume (RAVi) and reservoir strain (RAS), remains to be clarified. Accordingly, the aim of our study is to investigate the association with outcome of RAR in patients with STR.

Circulating Small Extracellular Vesicles Reflect the Severity of Myocardial Damage in STEMI Patients.

Circulating small extracellular vesicles (sEVs) contribute to inflammation, coagulation and vascular injury, and have great potential as diagnostic markers of disease. The ability of sEVs to reflect myocardial damage assessed by Cardiac Magnetic Resonance (CMR) in ST-segment elevation myocardial infarction (STEMI) is unknown. To fill this gap, plasma sEVs were isolated from 42 STEMI patients treated by primary percutaneous coronary intervention (pPCI) and evaluated by CMR between days 3 and 6.

A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images.

Aims: Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.

Cardiac computed tomography: from anatomy to function.

Ischaemic heart disease (IHD) is one of the world's leading causes of morbidity and mortality. Likewise, the diagnosis and risk stratification of patients with coronary artery disease (CAD) have always been based on the detection of the presence and extent of ischaemia by physical or pharmacological stress tests with or without the aid of imaging methods (e.g.

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images.

Background And Objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed.

Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included.

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

Background: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).

Methods: In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model.

Comparison between Automatic and Semiautomatic System for the 3D Echocardiographic Multiparametric Evaluation of RV Function and Dimension.

Background: The right ventricle (RV) plays a pivotal role in cardiovascular diseases and 3-dimensional echocardiography (3DE) has gained acceptance for the evaluation of RV volumes and function. Recently, a new artificial intelligence (AI)-based automated 3DE software for RV evaluation has been proposed and validated against cardiac magnetic resonance. The aims of this study were three-fold: (i) feasibility of the AI-based 3DE RV quantification, (ii) comparison with the semi-automatic 3DE method and (iii) assessment of 2-dimensional echocardiography (2DE) and strain measurements obtained automatically.

Decision Support Systems in HF based on Deep Learning Technologies.

Purpose Of Review: Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field.

Recent Findings: DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods.

Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair.

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence.

Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled.

Head to Head Comparison between Different 3-Dimensional Echocardiographic Rendering Tools in the Imaging of Percutaneous Edge-to-Edge Mitral Valve Repair.

MitraClip (MC) is the most common percutaneous treatment for severe mitral regurgitation (MR). An accurate two-dimensional and three-dimensional echocardiographic (3DTEE) imaging is mandatory for the optimal procedural result. Recently transillumination 3DTEE rendering (3DTr) has been introduced integrating a virtual light source into the dataset and with the addition of glass effect (3DGl) allows to adjust tissue transparency improving depth perception and anatomical structure delineation in comparison with the standard 3DTEE (3DSt).

Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques.

Background: Whereas transcatheter aortic valve implantation (TAVI) has become the gold standard for aortic valve stenosis treatment in high-risk patients, it has recently been extended to include intermediate risk patients. However, the mortality rate at 5 years is still elevated. The aim of the present study was to develop a novel machine learning (ML) approach able to identify the best predictors of 5-year mortality after TAVI among several clinical and echocardiographic variables, which may improve the long-term prognosis.

Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network.

Background And Objective: Segmentation of the left ventricular (LV) myocardium (Myo) and RV endocardium on cine cardiac magnetic resonance (CMR) images represents an essential step for cardiac-function evaluation and diagnosis. In order to have a common reference for comparing segmentation algorithms, several CMR image datasets were made available, but in general they do not include the most apical and basal slices, and/or gold standard tracing is limited to only one of the two ventricles, thus not fully corresponding to real clinical practice. Our aim was to develop a deep learning (DL) approach for automated segmentation of both RV and LV chambers from short-axis (SAX) CMR images, reporting separately the performance for basal slices, together with the applied criterion of choice.

Novelties in 3D Transthoracic Echocardiography.

Cardiovascular imaging is developing at a rapid pace and the newer modalities, in particular three-dimensional echocardiography, allow better analysis of heart structures. Identifying valve lesions and grading their severity represents crucial information and nowadays is strengthened by the introduction of new software, such as transillumination, which provide detailed morphology descriptions. Chambers quantification has never been so rapid and accurate: machine learning algorithms generate automated volume measurements, including left ventricular systolic and diastolic function, which is extremely important for clinical decisions.