AIMC Topic: Echocardiography

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(Deep) Learning Your Left From Your Right.

JACC. Cardiovascular imaging May 15, 2019
Humans Echocardiography Deep Learning Myocardial Contraction
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Flow network tracking for spatiotemporal and periodic point matching: Applied to cardiac motion analysis.

Medical image analysis Apr 18, 2019
The accurate quantification of left ventricular (LV) deformation/strain shows significant promise for quantitatively assessing cardiac function for use in diagnosis and therapy planning. However, accurate estimation of the displacement of myocardial ...
Dogs Motion Animals Ventricular Dysfunction, Left Neural Networks, Computer Echocardiography Algorithms Imaging, Three-Dimensional Image Processing, Computer-Assisted
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New Multiparametric Analysis of Cardiac Dyssynchrony: Machine Learning and Prediction of Response to CRT.

JACC. Cardiovascular imaging Apr 17, 2019
Electrocardiography Treatment Outcome Predictive Value of Tests Signal Processing, Computer-Assisted Clinical Decision-Making Ventricular Function, Left Echocardiography Heart Failure Cardiac Resynchronization Therapy Ventricular Dysfunction, Left Image Interpretation, Computer-Assisted Humans Machine Learning Decision Support Techniques Retrospective Studies
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Comparisons of efficacy, safety, and recurrence risk factors of paroxysmal and persistent atrial fibrillation catheter ablation using robotic magnetic navigation system.

Clinical cardiology Mar 15, 2019
BACKGROUND: No data exist on comparisons of efficacy, safety, and recurrence risk factors of paroxysmal and persistent atrial fibrillation (AF) ablation using robotic magnetic navigation system (MNS), respectively.
Adolescent Treatment Outcome Follow-Up Studies Robotics Male Echocardiography Middle Aged Heart Atria Heart Rate Catheter Ablation Recurrence Equipment Design Surgery, Computer-Assisted Heart Conduction System Atrial Fibrillation Prospective Studies Tomography, X-Ray Computed Female Risk Factors Aged Young Adult Adult Humans Pulmonary Veins
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Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.

Journal of cardiovascular electrophysiology Mar 10, 2019
OBJECTIVES: We sought to validate a deep learning algorithm designed to predict an ejection fraction (EF) less than or equal to 35% based on the 12-lead electrocardiogram (ECG) in a large prospective cohort.
Signal Processing, Computer-Assisted Heart Rate Ventricular Dysfunction, Left Deep Learning Electrocardiography Predictive Value of Tests Aged Prospective Studies Aged, 80 and over Prognosis Humans Stroke Volume Male Female Echocardiography Ventricular Function, Left Reproducibility of Results Middle Aged Diagnosis, Computer-Assisted
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Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

Artificial intelligence in medicine Mar 8, 2019
The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT ...
Neural Networks, Computer Female Adult Male Echocardiography Middle Aged Hemodynamics Humans Image Processing, Computer-Assisted Cardiography, Impedance
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Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography.

IEEE transactions on medical imaging Feb 22, 2019
Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far...
Deep Learning Image Processing, Computer-Assisted Heart Humans Algorithms Databases, Factual Echocardiography
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Contribution of Cardiovascular Reserve to Prognostic Categories of Heart Failure With Preserved Ejection Fraction: A Classification Based on Machine Learning.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Feb 1, 2019
BACKGROUND: The authors used cluster analysis of data from cardiovascular domains associated with exercise intolerance to help define prognostic phenotypes of patients with heart failure with preserved ejection fraction (HFpEF).
Heart Failure Exercise Tolerance Heart Function Tests Humans Middle Aged Longitudinal Studies Machine Learning Aged Exercise Test Phenotype Biomarkers Male Prospective Studies Stroke Volume Prognosis Echocardiography Female
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Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss.

IEEE transactions on medical imaging Dec 24, 2018
Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is...
Deep Learning Humans Image Processing, Computer-Assisted Algorithms Heart Echocardiography Myocardial Contraction
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Machine Learning for Data-Driven Discovery: The Rise and Relevance.

JACC. Cardiovascular imaging Dec 12, 2018
Machine Learning Electronic Health Records Echocardiography
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