AIMC Topic: Echocardiography
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Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.
Aging
May 16, 2024
BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aim...
Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality.
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
May 11, 2024
BACKGROUND: Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transtho...
Machine Learning Quantification of Pulmonary Regurgitation Fraction from Echocardiography.
Pediatric cardiology
May 10, 2024
Assessment of pulmonary regurgitation (PR) guides treatment for patients with congenital heart disease. Quantitative assessment of PR fraction (PRF) by echocardiography is limited. Cardiac MRI (cMRI) is the reference-standard for PRF quantification. ...
A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.
Scientific reports
May 9, 2024
To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based...
Interpretation of SPECT wall motion with deep learning.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
May 7, 2024
OBJECTIVES: We sought to develop a novel deep learning (DL) workflow to interpret single-photon emission computed tomography (SPECT) wall motion.
Predictors of Disease Progression and Adverse Clinical Outcomes in Patients With Moderate Aortic Stenosis Using an Artificial Intelligence-Based Software Platform.
The American journal of cardiology
May 6, 2024
Patients with moderate aortic stenosis (AS) have a greater risk of adverse clinical outcomes than that of the general population. How this risk compares with those with severe AS, along with factors associated with outcomes and disease progression, i...
Cardiac Valve Event Timing in Echocardiography Using Deep Learning and Triplane Recordings.
IEEE journal of biomedical and health informatics
May 6, 2024
Cardiac valve event timing plays a crucial role when conducting clinical measurements using echocardiography. However, established automated approaches are limited by the need of external electrocardiogram sensors, and manual measurements often rely ...
Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.
ESC heart failure
May 3, 2024
AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. ...
An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography.
Ultrasound in medicine & biology
May 1, 2024
BACKGROUND: Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associated with an increased risk of neurological disorders, specifically cryptogenic stroke. Saline-contrasted transthoracic echocardiography (scTTE) is ofte...
A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning.
Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese
Apr 16, 2024
BACKGROUND: To develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms.
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