AIMC Topic: Echocardiography

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

The American journal of emergency medicine Oct 15, 2024
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...
Prospective Studies Female Aged Sensitivity and Specificity Humans Artificial Intelligence Heart Failure, Diastolic Diastole Male Point-of-Care Systems Echocardiography Middle Aged Emergency Service, Hospital Systole
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Segmentation of four-chamber view images in fetal ultrasound exams using a novel deep learning model ensemble method.

Computers in biology and medicine Oct 11, 2024
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 ...
Deep Learning Image Processing, Computer-Assisted Fetal Heart Humans Echocardiography Pregnancy Female Ultrasonography, Prenatal
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Utilizing echocardiography and unsupervised machine learning for heart failure risk identification.

International journal of cardiology Oct 10, 2024
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...
Humans Female Aged Adult Risk Assessment Male Unsupervised Machine Learning Follow-Up Studies Echocardiography Heart Failure Middle Aged
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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 Sep 30, 2024
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 ...
Male Echocardiography Middle Aged Severity of Illness Index Delivery of Health Care, Integrated Natural Language Processing Female Humans Heart Valve Diseases Algorithms Aged California Adult
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SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning.

Ultrasound in medicine & biology Sep 28, 2024
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...
Heart Ventricles Supervised Machine Learning Humans Echocardiography Image Processing, Computer-Assisted
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Robotic navigation with deep reinforcement learning in transthoracic echocardiography.

International journal of computer assisted radiology and surgery Sep 20, 2024
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 ...
Surgery, Computer-Assisted Mitral Valve Reinforcement Machine Learning Echocardiography Deep Learning Robotic Surgical Procedures Humans Algorithms
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Deep Learning Model of Diastolic Dysfunction Risk Stratifies the Progression of Early-Stage Aortic Stenosis.

JACC. Cardiovascular imaging Sep 18, 2024
BACKGROUND: The development and progression of aortic stenosis (AS) from aortic valve (AV) sclerosis is highly variable and difficult to predict.
Risk Factors Aged Male Middle Aged Risk Assessment Ventricular Function, Left Echocardiography Aortic Valve Prognosis Predictive Value of Tests Female Humans Ventricular Dysfunction, Left Reproducibility of Results Time Factors Disease Progression Calcinosis Diastole United States Severity of Illness Index Aortic Valve Stenosis Sclerosis Deep Learning
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Automatic segmentation of echocardiographic images using a shifted windows vision transformer architecture.

Biomedical physics & engineering express Sep 13, 2024
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...
Echocardiography Image Processing, Computer-Assisted Heart Heart Ventricles Humans Neural Networks, Computer Algorithms Heart Defects, Congenital
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A deep learning phase-based solution in 2D echocardiography motion estimation.

Physical and engineering sciences in medicine Sep 12, 2024
In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from ...
Humans Algorithms Echocardiography Wavelet Analysis Movement Deep Learning Image Processing, Computer-Assisted
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Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

PloS one Sep 12, 2024
BACKGROUND: Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echoca...
Echocardiography Algorithms Humans Heart Ventricles Neural Networks, Computer Deep Learning Image Interpretation, Computer-Assisted Female Machine Learning Myocardial Infarction Male
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