We sought to prospectively investigate the accuracy of an artificial intelligence (AI)-based tool for left ventricular ejection fraction (LVEF) assessment using a hand-held ultrasound device (HUD) in COVID-19 patients and to examine whether reduced LVEF predicts the composite endpoint of in-hospital death, advanced ventilatory support, shock, myocardial injury, and acute decompensated heart failure. COVID-19 patients were evaluated with a real-time LVEF assessment using an HUD equipped with an AI-based tool vs. assessment by a blinded fellowship-trained echocardiographer.
View Article and Find Full Text PDFIntroduction: Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making.
Aim: To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department.
Background: Transcatheter edge-to-edge mitral valve repair (TEER) has been established as a therapy for severe symptomatic mitral regurgitation (MR) in stable patients, and it has recently emerged as a reasonable option for acutely ill patients. The aim of this study was to evaluate the safety and efficacy of TEER in hospitalized patients with acute decompensated heart failure (ADHF) and severe MR that was deemed to play a major role in their deterioration.
Methods: We included 31 patients who underwent emergent TEER for MR ≥ 3+ from 2012 to 2022 at Sheba Medical Center.