AIMC Topic: Echocardiography

Clear Filters Showing 81 to 90 of 430 articles

External evaluation of a commercial artificial intelligence-augmented digital auscultation platform in valvular heart disease detection using echocardiography as reference standard.

International journal of cardiology
OBJECTIVE: There are few studies evaluating the accuracy of commercially available AI-powered digital auscultation platforms in detecting valvular heart disease (VHD). Therefore, the utility of these systems for diagnosing clinically significant VHD ...

Contour-constrained branch U-Net for accurate left ventricular segmentation in echocardiography.

Medical & biological engineering & computing
Using echocardiography to assess the left ventricular function is one of the most crucial cardiac examinations in clinical diagnosis, and LV segmentation plays a particularly vital role in medical image processing as many important clinical diagnosti...

Diagnostic accuracy of artificial intelligence for identifying systolic and diastolic cardiac dysfunction in the emergency department.

The American journal of emergency medicine
INTRODUCTION: Cardiac point-of-care ultrasound (POCUS) can evaluate for systolic and diastolic dysfunction to inform care in the Emergency Department (ED). However, accurate assessment can be limited by user experience. Artificial intelligence (AI) h...

Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method.

Computers in biology and medicine
Fetal echocardiography, a specialized ultrasound application commonly utilized for fetal heart assessment, can greatly benefit from automated segmentation of anatomical structures, aiding operators in their evaluations. We introduce a novel approach ...

Utilizing echocardiography and unsupervised machine learning for heart failure risk identification.

International journal of cardiology
BACKGROUND: Global longitudinal strain (GLS) is recognized as a powerful predictor of heart failure (HF). However, the entire strain curve may entail important prognostic information regarding HF risk that might be undiscovered by only focusing on th...

Identifying the Severity of Heart Valve Stenosis and Regurgitation Among a Diverse Population Within an Integrated Health Care System: Natural Language Processing Approach.

JMIR cardio
BACKGROUND: Valvular heart disease (VHD) is a leading cause of cardiovascular morbidity and mortality that poses a substantial health care and economic burden on health care systems. Administrative diagnostic codes for ascertaining VHD diagnosis are ...

SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning.

Ultrasound in medicine & biology
OBJECTIVE: Achieving reliable automatic left ventricle (LV) segmentation from echocardiograms is challenging due to the inherent sparsity of annotations in the dataset, as clinicians typically only annotate two specific frames for diagnostic purposes...

Robotic navigation with deep reinforcement learning in transthoracic echocardiography.

International journal of computer assisted radiology and surgery
PURPOSE: The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient ...

Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture.

Biomedical physics & engineering express
Echocardiography is one the most commonly used imaging modalities for the diagnosis of congenital heart disease. Echocardiographic image analysis is crucial to obtaining accurate cardiac anatomy information. Semantic segmentation models can be used t...