Artificial Intelligence Automation of Echocardiographic Measurements.

Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.

Objectives: The purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.

Impact of cardiac rehabilitation exercise management on improving exercise tolerance among patients with chronic heart disease using a telemetry-based biosignal measurement device: protocol for a multicentre randomised controlled trial in Japan (iCARE-MATE study).

Introduction: Cardiac rehabilitation (CR) improves the outcomes of patients with chronic heart disease. However, participation in hospital-based programs is limited, partly because of accessibility issues. Notably, most CRs are performed unsupervised at home, making it challenging for healthcare providers and patients to monitor progress accurately or provide tailored feedback.

Automated coronary artery segmentation / tissue characterization and detection of lipid-rich plaque: An integrated backscatter intravascular ultrasound study.

Background: Intravascular ultrasound (IVUS)-based tissue characterization has been used to detect vulnerable plaque or lipid-rich plaque (LRP). Recently, advancements in artificial intelligence (AI) technology have enabled automated coronary arterial plaque segmentation and tissue characterization. The purpose of this study was to evaluate the feasibility and diagnostic accuracy of a deep learning model for plaque segmentation, tissue characterization and identification of LRP.

Using Deep Learning to Predict Cardiovascular Magnetic Resonance Findings From Echocardiographic Videos.

Background: Echocardiography is the most common modality for assessing cardiac structure and function. Although cardiac magnetic resonance (CMR) imaging is less accessible, it can provide unique tissue characterization, including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV), which are associated with tissue fibrosis, infiltration, and inflammation. Deep learning has been shown to uncover findings not recognized by clinicians, but it is unknown whether CMR-based tissue characteristics can be derived from echocardiographic videos using deep learning.

Automated Deep Learning Pipeline for Characterizing Left Ventricular Diastolic Function.

Introduction: Left ventricular diastolic dysfunction (LVDD) is most commonly evaluated by echocardiography. However, without a sole identifying metric, LVDD is assessed by a diagnostic algorithm relying on secondary characteristics that is laborious and has potential for interobserver variability.

Methods: To characterize concordance in clinical evaluations of LVDD, we evaluated historical echocardiogram studies at two academic medical centers for variability between clinician text reports and assessment by 2016 American Society of Echocardiography (ASE) guidelines.

Artificial Intelligence Prediction of Age from Echocardiography as a Marker for Cardiovascular Disease.

Accurate understanding of biological aging and the impact of environmental stressors is crucial for understanding cardiovascular health and identifying patients at risk for adverse outcomes. Chronological age stands as perhaps the most universal risk predictor across virtually all populations and diseases. While chronological age is readily discernible, efforts to distinguish between biologically older versus younger individuals can, in turn, potentially identify individuals with accelerated versus delayed cardiovascular aging.

Artificial intelligence automation of echocardiographic measurements.

Background: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time, however manual assessment is time-consuming and can be imprecise. Artificial intelligence (AI) has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.

Methods: We developed and validated open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.

Automated Aortic Regurgitation Detection and Quantification: A Deep Learning Approach Using Multi-View Echocardiography.

Background: Accurate evaluation of aortic regurgitation (AR) severity is necessary for early detection and chronic disease management. AR is most commonly assessed by Doppler echocardiography, however limitations remain given variable image quality and need to integrate information from multiple views. This study developed and validated a deep learning model for automated AR severity assessment from multi-view color Doppler videos.

AI-echocardiography: Current status and future direction.

Echocardiography, which provides detailed evaluations of cardiac structure and pathology, is central to cardiac imaging. Traditionally, the assessment of disease severity, treatment effectiveness, and prognosis prediction relied on detailed parameters obtained by trained sonographers and the expertise of specialists, which can limit access and availability. Recent advancements in deep learning and large-scale computing have enabled the automatic acquisition of parameters in a short time using vast amounts of historical training data.

Effectiveness of rate-adaptive pacing on patients with chronotropic incompetence: Systematic review and meta-analysis of randomized controlled trials.

Background: Rate-adaptive pacing (RAP) complements heart rate (HR) responses in patients with cardiac pacing devices and chronotropic incompetence, although improvements in exercise capacity have varied across reported studies. The purpose of this study was to evaluate the effectiveness of the RAP mode across different clinical settings.

Methods: A systematic review and meta-analysis were conducted according to PRISMA guidelines.

Automated Evaluation for Pericardial Effusion and Cardiac Tamponade with Echocardiographic Artificial Intelligence.

Background: Timely and accurate detection of pericardial effusion and assessment cardiac tamponade remain challenging and highly operator dependent.

Objectives: Artificial intelligence has advanced many echocardiographic assessments, and we aimed to develop and validate a deep learning model to automate the assessment of pericardial effusion severity and cardiac tamponade from echocardiogram videos.

Methods: We developed a deep learning model (EchoNet-Pericardium) using temporal-spatial convolutional neural networks to automate pericardial effusion severity grading and tamponade detection from echocardiography videos.

Vitamin D deficiency stratifies the risk of covert and overt hepatic encephalopathy in patients with cirrhosis: A retrospective cohort study.

Backgrounds & Aims: This study aimed to investigate the association between vitamin D deficiency and covert hepatic encephalopathy (CHE), overt hepatic encephalopathy (OHE) occurrence, and mortality in patients with cirrhosis.

Methods: This retrospective study reviewed 679 patients with cirrhosis. Vitamin D deficiency was defined as serum 25-hydorxyvitamin D (25-OHD) levels < 20 ng/mL.

The significance of non-sustained ventricular tachycardia on life-threatening ventricular arrhythmia events in patients with non-ischemic cardiomyopathy in the contemporary era: A systematic review and meta-analysis.

Background: Risk stratification for patients with non-ischemic cardiomyopathy (NICM) remains challenging as previous studies predicting life-threatening ventricular arrhythmia (LTVA) events were conducted before the establishment of the current standard treatment. We investigated the prognostic value of non-sustained ventricular tachycardia (NSVT) in NICM patients among recent studies.

Methods: MEDLINE, Embase were searched from January 2000 to October 2023.

Comparative Evaluation of Alcohol Septal Ablation: Left Anterior Descending Versus Non-left Anterior Descending Artery Approaches.

Alcohol septal ablation (ASA) is performed for symptomatic drug-refractory hypertrophic obstructive cardiomyopathy to reduce the left ventricular outflow tract pressure gradient (LVOTPG) by injecting ethanol into a septal branch that perforates the septal bulge. The target septal branches usually arise directly from the left anterior descending (LAD) artery; however, vessels from a non-LAD artery can be selected in some cases. This study aimed to compare the effectiveness and safety between ASA performed using a septal branch arising from a non-LAD artery and a branch arising from the LAD artery.

Inappropriate shock incidence in patients with subcutaneous implantable cardioverter-defibrillators with concomitant cardiac implantable electronic devices: A single-center cohort study.

Background: Subcutaneous implantable cardioverter defibrillators (S-ICDs) are occasionally used in combination with other cardiac implantable electronic devices (CIEDs). However, whether the incidence of inappropriate shock increases in patients with S-ICDs and concomitant CIEDs remains unclear. This study aimed to investigate the association between the concomitant use of CIEDs and the incidence of inappropriate shock in patients with current-generation S-ICDs.

Impact of Tweet Content on the Number of Retweets - "Tweet the Meeting 2022".

Previous research has investigated the effectiveness of the "Tweet the Meeting" campaign, but the relationship between tweet content and the number of retweets has not been fully evaluated. We analyzed the number of tweets and retweets during the Japanese Circulation Society's 2022 annual meeting. The ambassador group had significantly more session- and symposium-related tweets than the non-ambassador group (P<0.

Thirty-day readmission rate of same-day discharge protocol after left atrial appendage occlusion: A propensity score-matched analysis from the National Readmission Database.

Background: Given the reduction in periprocedural complication rates, same-day discharge (SDD) after percutaneous left atrial appendage closure (LAAC) could be beneficial. To date, little data exist comparing the standard overnight stay (ONS) vs SDD after LAAC.

Objective: The purpose of this study was to investigate the safety and efficacy of SDD compared with ONS.

Inverse Association between Fruit and Vegetable Intake and All-Cause Mortality: Japan Public Health Center-Based Prospective Study.

Background: A dose-response and nonlinear association between fruit and vegetable intake and mortality has been reported in Europe and the United States, but little is known about this association in Asia.

Objectives: This study aimed to evaluate the association of fruit and vegetable intake with all-cause, cancer, cardiovascular, and respiratory disease mortality in a Japanese cohort.

Methods: In the Japan Public Health Center-based prospective study, we included 94,658 participants (mean age: 56.

QRS and cycle length alternans in a patient with idiopathic left ventricular tachycardia: Postulated mechanism and clinical implications.

Idiopathic left ventricular tachycardia is macro-reentrant tachycardia involving the fascicles in the left ventricle as a part of its reentrant circuit. The detailed circuit mechanisms somewhat remain unclear. We reported QRS and cycle length alternans confirmed after the first application of radiofrequency delivery for the distal site of left posterior fascicle potential (P2) in a patient with idiopathic left ventricular tachycardia.