Three-Dimensional Transthoracic Echocardiography for Semiautomated Analysis of the Tricuspid Annulus: Validation and Normal Values.

Background: The expansion of tricuspid valve (TV) interventions has underscored the need for accurate and reproducible three-dimensional (3D) transthoracic echocardiographic (TTE) tools for evaluating the tricuspid annulus and for 3D normal values of this structure. The aims of this study were to develop new semi-automated software for 3D TTE analysis of the tricuspid annulus, compare its accuracy and reproducibility against those of multiplanar reconstruction (MPR) reference, and determine normative values.

Methods: Three-dimensional TTE images of 113 patients with variable degrees of tricuspid regurgitation were analyzed using the new semiautomated software and conventional MPR methodology (as the reference standard), each by three independent readers.

Real-Time Artificial Intelligence-Based Guidance of Echocardiographic Imaging by Novices: Image Quality and Suitability for Diagnostic Interpretation and Quantitative Analysis.

Background: We aimed to assess in a prospective multicenter study the quality of echocardiographic exams performed by inexperienced users guided by a new artificial intelligence software and evaluate their suitability for diagnostic interpretation of basic cardiac pathology and quantitative analysis of cardiac chamber and function.

Methods: The software (UltraSight, Ltd) was embedded into a handheld imaging device (Lumify; Philips). Six nurses and 3 medical residents, who underwent minimal training, scanned 240 patients (61±16 years; 63% with cardiac pathology) in 10 standard views.

Deep learning assisted measurement of echocardiographic left heart parameters: improvement in interobserver variability and workflow efficiency.

Machine learning techniques designed to recognize views and perform measurements are increasingly used to address the need for automation of the interpretation of echocardiographic images. The current study was designed to determine whether a recently developed and validated deep learning (DL) algorithm for automated measurements of echocardiographic parameters of left heart chamber size and function can improve the reproducibility and shorten the analysis time, compared to the conventional methodology. The DL algorithm trained to identify standard views and provide automated measurements of 20 standard parameters, was applied to images obtained in 12 randomly selected echocardiographic studies.

Noninvasive assessment of left ventricular end-diastolic pressure using machine learning-derived phasic left atrial strain.

Aims: While transthoracic echocardiography (TTE) assessment of left ventricular end-diastolic pressure (LVEDP) is critically important, the current paradigm is subject to error and indeterminate classification. Recently, peak left atrial strain (LAS) was found to be associated with LVEDP. We aimed to test the hypothesis that integration of the entire LAS time curve into a single parameter could improve the accuracy of peak LAS in the noninvasive assessment of LVEDP with TTE.

Preservation of Circumferential and Radial Left Ventricular Function as a Mitigating Mechanism for Impaired Longitudinal Strain in Early Cardiac Amyloidosis.

Background: In patients with cardiac amyloidosis (CA), left ventricular ejection fraction (LVEF) is frequently preserved, despite commonly reduced global longitudinal strain (GLS). We hypothesized that nonlongitudinal contraction may initially serve as a mitigating mechanism to maintain cardiac output and studied the relationship between global circumferential (GCS) and radial (GRS) strain with LVEF and extracellular volume (ECV), a marker of amyloid burden.

Methods: Patients with CA who underwent cardiac magnetic resonance (CMR; n = 140, 70.

How Often Does Apical Sparing of Longitudinal Strain Indicate the Presence of Cardiac Amyloidosis?

Echocardiographic diagnosis of cardiac amyloidosis (CA) is frequently suggested by the presence of a left ventricular (LV) apical sparing pattern (ASP) on longitudinal strain (LS) assessment, the so-called "cherry on top" pattern, defined by strain magnitude preserved exclusively at the apex. However, it is unclear how frequently this strain pattern truly represents CA. This study aimed to evaluate the predictive value of ASP in the diagnosis of CA.

Normal Values of Three-Dimensional Right Ventricular Size and Function Measurements: Results of the World Alliance Societies of Echocardiography Study.

Background: Normal values for three-dimensional (3D) right ventricular (RV) size and function are not well established, as they originate from small studies that involved predominantly white North American and European populations, did not use RV-focused views, and relied on older 3D RV analysis software. The World Alliance Societies of Echocardiography study was designed to generate reference ranges for normal subjects around the world. The aim of this study was to assess the worldwide capability of 3D imaging of the right ventricle and report size and function measurements, including their dependency on age, sex, and ethnicity.

Age-, Sex-, and Race-Based Normal Values for Left Ventricular Circumferential Strain from the World Alliance Societies of Echocardiography Study.

Background: Left ventricular (LV) circumferential strain has received less attention than longitudinal deformation, which has recently become part of routine clinical practice. Among other reasons, this is because of the lack of established normal values. Accordingly, the aim of this study was to establish normative values for LV circumferential strain and determine sex-, age-, and race-related differences in a large cohort of healthy adults.

Normal Values of Left Ventricular Mass by Two-Dimensional and Three-Dimensional Echocardiography: Results from the World Alliance Societies of Echocardiography Normal Values Study.

Background: Although increased left ventricular (LV) mass is associated with adverse outcomes, measured values vary widely depending on the specific technique used. Moreover, the impact of sex, age, and race on LV mass remains controversial, further limiting the clinical use of this parameter. Accordingly, the authors studied LV mass using a variety of two-dimensional and three-dimensional echocardiographic techniques in a large population of normal subjects encompassing a wide range of ages.

Impact of wideband cardiac magnetic resonance on diagnosis, decision-making and outcomes in patients with implantable cardioverter defibrillators.

Aims: Although myocardial scar assessment using late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is frequently indicated for patients with implantable cardioverter defibrillators (ICDs), metal artefact can degrade image quality. With the new wideband technique designed to mitigate device related artefact, CMR is increasingly used in this population. However, the common clinical indications for CMR referral and impact on clinical decision-making and prognosis are not well defined.

Normative values of the aortic valve area and Doppler measurements using two-dimensional transthoracic echocardiography: results from the Multicentre World Alliance of Societies of Echocardiography Study.

Aims: Aortic valve area (AVA) used for echocardiographic assessment of aortic stenosis (AS) has been traditionally interpreted independently of sex, age and race. As differences in normal values might impact clinical decision-making, we aimed to establish sex-, age- and race-specific normative values for AVA and Doppler parameters using data from the World Alliance Societies of Echocardiography (WASE) Study.

Methods And Results: Two-dimensional transthoracic echocardiographic studies were obtained from 1903 healthy adult subjects (48% women).

Three-Dimensional Echocardiographic Deconstruction: Feasibility of Clinical Evaluation from Two-Dimensional Views Derived from a Three-Dimensional Data Set.

Background: Three-dimensional echocardiography (3DE) makes it possible to capture the entire heart in a single data set that theoretically could be used to extract any two-dimensional (2D) views and potentially replace the standard practice of serial 2D acquisitions. The aim of this study was to test the hypothesis that the quality of 3DE-derived 2D images is sufficient to allow the visualization of the left ventricular (LV), right ventricular (RV), and left atrial (LA) endocardium, on par with images from conventional two-dimensional echocardiography (2DE), and potentially more accurate quantification of chamber size and function.

Methods: First, the investigators prospectively studied 36 patients who underwent 2DE in 14 standard views, and full-volume data sets from 3DE, from which the same views were extracted offline.

Relation of Myocardial Perfusion Reserve and Left Ventricular Ejection Fraction in Ischemic and Nonischemic Cardiomyopathy.

Quantification of myocardial perfusion reserve (MPR) using vasodilator stress cardiac magnetic resonance is increasingly used to detect coronary artery disease. However, MPR can also be altered because of changes in microvascular function. We aimed to determine whether MPR can distinguish between ischemic cardiomyopathy (IC) secondary to coronary artery disease and non-IC (NIC) with microvascular dysfunction and no underlying epicardial coronary disease.

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.

Background: Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development.

Normal Values of Left Ventricular Size and Function on Three-Dimensional Echocardiography: Results of the World Alliance Societies of Echocardiography Study.

Background: Echocardiography remains the most widely used modality to assess left ventricular (LV) chamber size and function. Currently this assessment is most frequently performed using two-dimensional (2D) echocardiography. However, three-dimensional (3D) echocardiography has been shown to be more accurate and reproducible than 2D echocardiography.

Can echocardiographic assessment of diastolic function be automated?

Echocardiographic evaluation of left ventricular diastolic function relies on a multi-pronged algorithm, which incorporates Doppler-based and volumetric parameters. Integration of clinical data in diastolic assessment is recommended, though not clearly outlined. We sought to develop an automated tool for diastolic function, compare its performance to human-generated diagnoses and identify the common sources of error.

Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images.

Background: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms.

AI Based CMR Assessment of Biventricular Function: Clinical Significance of Intervendor Variability and Measurement Errors.

Objectives: The aim of this study was to determine whether left ventricular ejection fraction (LVEF) and right ventricular ejection fraction (RVEF) and left ventricular mass (LVM) measurements made using 3 fully automated deep learning (DL) algorithms are accurate and interchangeable and can be used to classify ventricular function and risk-stratify patients as accurately as an expert.

Background: Artificial intelligence is increasingly used to assess cardiac function and LVM from cardiac magnetic resonance images.

Methods: Two hundred patients were identified from a registry of individuals who underwent vasodilator stress cardiac magnetic resonance.