Journal of the American Heart Association
Feb 8, 2022
Background Global longitudinal shortening (GL-Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of autom...
BACKGROUND: Heart failure with preserved ejection fraction (HFpEF), affected collectively by genetic and environmental factors, is the common subtype of chronic heart failure. Although the available risk assessment methods for HFpEF have achieved som...
OBJECTIVE: The purpose of this study was to explore a new algorithm model capable of leverage deep learning to screen and diagnose specific types of left-to-right shunt congenital heart disease (CHD) in children.
BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm sh...
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Nov 24, 2021
OBJECTIVES: A paucity of point-of-care ultrasound (POCUS) databases limits machine learning (ML). Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apica...
PURPOSE: Cardiovascular magnetic resonance (CMR) is a vital diagnostic tool in the management of cardiovascular diseases. The advent of advanced CMR technologies combined with artificial intelligence (AI) has the potential to simplify imaging, reduce...
OBJECTIVES: This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.
OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of experiencing adverse cardiac events. A machine learning (ML) method could enable more effective risk stra...
BACKGROUND: Mortality remains unacceptably high in patients with heart failure and reduced left ventricular ejection fraction (LVEF) despite advances in therapeutics. We hypothesised that a novel artificial intelligence approach could better assess m...
BACKGROUND: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW).
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.