AIMC Topic: Heart Atria
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Design and Validation of a Soft Robotic Simulator for Transseptal Puncture Training.
IEEE transactions on bio-medical engineering
Sep 27, 2023
OBJECTIVE: Transseptal puncture (TP) is the technique used to access the left atrium of the heart from the right atrium during cardiac catheterization procedures. Through repetition, electrophysiologists and interventional cardiologists experienced i...
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.
IEEE journal of biomedical and health informatics
Jun 30, 2023
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of th...
Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation.
Computers in biology and medicine
Dec 14, 2022
To improve the quality of magnetic resonance (MR) image edge segmentation, some researchers applied additional edge labels to train the network to extract edge information and aggregate it with region information. They have made significant progress....
Machine learning based deconvolution of microarray atrial samples from atrial fibrillation patients reveals increased fractions of follicular CD4+ T lymphocytes and gamma-delta T cells.
Journal of physiology and pharmacology : an official journal of the Polish Physiological Society
Apr 24, 2022
A potential relationship between T cell immunity and development of atrial fibrillation (AF) has been proposed. Historically in AF patients it has been reported that peripheral blood had elevated CD4+ T cells. However few studies have explored whethe...
Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.
The international journal of cardiovascular imaging
Feb 24, 2022
Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess th...
Adaptive Hierarchical Dual Consistency for Semi-Supervised Left Atrium Segmentation on Cross-Domain Data.
IEEE transactions on medical imaging
Feb 2, 2022
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness...
Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes.
Journal of cardiovascular computed tomography
Dec 17, 2021
BACKGROUND: Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment a...
Detection of Left Atrial Myopathy Using Artificial Intelligence-Enabled Electrocardiography.
Circulation. Heart failure
Dec 16, 2021
BACKGROUND: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejection fraction and leads to the development of atrial fibrillation (AF). We investigated whether the likelihood of LA remodeling, LA dysfunction, altered ...
Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Nov 11, 2021
BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising acc...
A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.
BMC medical imaging
Mar 9, 2021
OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.
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