AIMC Topic: Stroke Volume
Clear Filters Showing 41 to 50 of 205 articles
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. ...
DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR.
Atherosclerosis
Apr 18, 2024
BACKGROUND AND AIMS: This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging.
Epicardial Adipose Tissue
Coronary Artery Disease
Myocardial Infarction
Pericardium
Magnetic Resonance Imaging, Cine
Ventricular Function, Left
Stroke Volume
Middle Aged
Risk Assessment
Deep Learning
Magnetic Resonance Imaging
Adipose Tissue
Predictive Value of Tests
Reproducibility of Results
Risk Factors
Aged
Male
Humans
Prognosis
Retrospective Studies
Female
Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.
ESC heart failure
Apr 18, 2024
Existing risk prediction models for hospitalized heart failure patients are limited. We identified patients hospitalized with a diagnosis of heart failure between 7 May 2013 and 26 April 2022 from a large academic, quaternary care medical centre (tra...
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.
Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning.
Scientific reports
Mar 29, 2024
Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular parameters, conventionally determined using cardiac magnetic resonance (CMR). However, given the high cost and limited availability of CMR in resource-constra...
Individualized Treatment Effect Prediction with Machine Learning - Salient Considerations.
NEJM evidence
Mar 26, 2024
BACKGROUND: Machine learning-based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition.
Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress.
Scientific reports
Feb 14, 2024
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed...
Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.
American heart journal
Feb 7, 2024
BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk a...
Pragmatic Evaluation of a Deep-Learning Algorithm to Automate Ejection Fraction on Hand-Held, Point-of-Care Echocardiography in a Cardiac Surgical Operating Room.
Journal of cardiothoracic and vascular anesthesia
Jan 11, 2024
OBJECTIVE: To test the correlation of ejection fraction (EF) estimated by a deep-learning-based, automated algorithm (Auto EF) versus an EF estimated by Simpson's method.
An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy.
Echocardiography (Mount Kisco, N.Y.)
Dec 21, 2023
AIM: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference.
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 30978
- Neural Networks, Computer 29696
- Algorithms 26813
- Deep Learning 26324
- Artificial Intelligence 24267
- Robotics 13307
- Image Processing, Computer-Assisted 9512
- Retrospective Studies 9097
- Magnetic Resonance Imaging 6173
- Reproducibility of Results 5589
- Tomography, X-Ray Computed 4765
- Support Vector Machine 4678
- Computational Biology 4229
- Brain 4045
- Computer Simulation 3877
Recent Journals
- Scientific reports 5865
- Sensors (Basel, Switzerland) 4794
- PloS one 4011
- Neural networks : the official journal of the International Neural Network Society 2928
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1839
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1278
- IEEE journal of biomedical and health informatics 1129
- Journal of chemical information and modeling 994
- Nature communications 938
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 861
- Briefings in bioinformatics 850
- IEEE transactions on neural networks and learning systems 823