AIMC Topic: Echocardiography
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AI derived ECG global longitudinal strain compared to echocardiographic measurements.
Scientific reports
Nov 2, 2024
Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial intelligence (AI)-generated electrocardiography score for LVGLS estimation (ECG-GLS score) to diagn...
Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language Processing.
JACC. Heart failure
Oct 23, 2024
BACKGROUND: The lack of automated tools for measuring care quality limits the implementation of a national program to assess guideline-directed care in heart failure with reduced ejection fraction (HFrEF).
A machine-learning algorithm to grade heart murmurs and stage preclinical myxomatous mitral valve disease in dogs.
Journal of veterinary internal medicine
Oct 21, 2024
BACKGROUND: The presence and intensity of heart murmurs are sensitive indicators of several cardiac diseases in dogs, particularly myxomatous mitral valve disease (MMVD), but accurate interpretation requires substantial clinical expertise.
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
Oct 19, 2024
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
Oct 17, 2024
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
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...
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 ...
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...
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 ...
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...
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