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A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.

Sensors (Basel, Switzerland)
Providing reliable detection of QRS complexes is key in automated analyses of electrocardiograms (ECG). Accurate and timely R-peak detections provide a basis for ECG-based diagnoses and to synchronize radiologic, electrophysiologic, or other medical ...

Heart sound classification based on equal scale frequency cepstral coefficients and deep learning.

Biomedizinische Technik. Biomedical engineering
Heart diseases represent a serious medical condition that can be fatal. Therefore, it is critical to investigate the measures of its early prevention. The Mel-scale frequency cepstral coefficients (MFCC) feature has been widely used in the early diag...

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.

Scientific reports
Recently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (...

Deep learning-based prediction of intra-cardiac blood flow in long-axis cine magnetic resonance imaging.

The international journal of cardiovascular imaging
PURPOSE: We aimed to design and evaluate a deep learning-based method to automatically predict the time-varying in-plane blood flow velocity within the cardiac cavities in long-axis cine MRI, validated against 4D flow.

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on...

Fast and robust parameter estimation with uncertainty quantification for the cardiac function.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Parameter estimation and uncertainty quantification are crucial in computational cardiology, as they enable the construction of digital twins that faithfully replicate the behavior of physical patients. Many model parameter...

Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.

IEEE transactions on medical imaging
Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imagi...

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Computational intelligence and neuroscience
Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning arc...

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics.

STAR protocols
Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capa...

Current and Future Applications of Artificial Intelligence in Cardiac CT.

Current cardiology reports
PURPOSE OF REVIEW: In this review, we aim to summarize state-of-the-art artificial intelligence (AI) approaches applied to cardiovascular CT and their future implications.