AIMC Topic: Echocardiography
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A comparative analysis of privacy-preserving large language models for automated echocardiography report analysis.
Journal of the American Medical Informatics Association : JAMIA
Jul 1, 2025
BACKGROUND: Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) gu...
AI-Driven View Guidance System in Intra-Cardiac Echocardiography Imaging.
IEEE transactions on bio-medical engineering
Jul 1, 2025
Intra-cardiac echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing real-time, high-resolution views from within the heart. Despite its advantages, effective ma...
Exploring interpretable echo analysis using self-supervised parcels.
Computers in biology and medicine
Jun 1, 2025
The application of AI for predicting critical heart failure endpoints using echocardiography is a promising avenue to improve patient care and treatment planning. However, fully supervised training of deep learning models in medical imaging requires ...
Eigenhearts: Cardiac diseases classification using eigenfaces approach.
Computers in biology and medicine
Jun 1, 2025
In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses. However, the integration of data science techniques in this field presents significant challenges, ...
Boundary-Enhanced $U^{2}$-Net for Simultaneous Four-Chamber Segmentation in Transthoracic Echocardiography.
IEEE journal of biomedical and health informatics
Jun 1, 2025
The heart, responsible for circulating blood throughout our body, contains four chambers. Existing analysis methods primarily focus on one single ventricle. Transthoracic echocardiography provides real-time estimations of cardiac function and enables...
Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.
Pediatric cardiology
Jun 1, 2025
Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnosis complications. Recent work demonstrated artificial intelligence-enhanced ECG analysis shows promise to detect ASD2 in adults. However, its applicat...
Artificial Intelligence Tools for Preconception Cardiomyopathy Screening Among Women of Reproductive Age.
Annals of family medicine
May 27, 2025
PURPOSE: Identifying cardiovascular disease before conception and in early pregnancy can better inform obstetric cardiovascular care. Our main objective was to evaluate the diagnostic performance of artificial intelligence (AI)-enabled digital tools ...
Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.
BMC pediatrics
May 24, 2025
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.
Understanding Transient Left Ventricular Ejection Fraction Reduction During Atrial Fibrillation With Artificial Intelligence.
Journal of the American Heart Association
May 20, 2025
BACKGROUND: Atrial fibrillation (AF) can cause a reduction in left ventricular ejection fraction (LVEF) that resolves rapidly upon restoration of sinus rhythm. We used artificial intelligence to understand (1) how often transient LVEF reduction durin...
Risk Stratification of Left Ventricle Hypertrabeculation Versus Non-Compaction Cardiomyopathy Using Echocardiography, Magnetic Resonance Imaging, and Cardiac Computed Tomography.
Echocardiography (Mount Kisco, N.Y.)
May 1, 2025
Non-compaction cardiomyopathy (NCCM) is a rare, congenital form of cardiomyopathy characterized by excessive trabeculations in the left ventricle myocardium. NCCM is often an underdiagnosed heart condition characterized by abnormal myocardial trabecu...
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