Arrhythmias

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone.

Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning.

The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (HIFU) ablation treatment of adenomyosis. However, t...

An ECG Signal Classification Method Based on Dilated Causal Convolution.

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are ti...

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing trans...

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or sy...

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This re...

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.

BACKGROUND: QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 201...

Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due ...

Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important...

Integrative transcriptomic, proteomic, and machine learning approach to identifying feature genes of atrial fibrillation using atrial samples from patients with valvular heart disease.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia with poorly understood mechanisms. We aimed to investigate the biological mechanism...

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management.

The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload of existing clinicians is consequently increasing...

Cardiac Severity Classification Using Pre Trained Neural Networks.

Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers h...

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning.

Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The ti...

Machine Learning for Clinical Outcome Prediction.

Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine le...

First-in-man application of a cold ablation robot guided laser osteotome in midface osteotomies.

The aim of the study was to assess the clinical applicability of robot guided laser osteotomy for clinical application. This is the initial report on ...

Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG-Derived Physiological Aging.

Background An artificial intelligence algorithm that detects age using the 12-lead ECG has been suggested to signal "physiologic age." This study aime...

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergenc...

Empirically constrained network models for contrast-dependent modulation of gamma rhythm in V1.

Gamma oscillations are thought to play a key role in neuronal network function and neuronal communication, yet the underlying generating mechanisms ha...

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers.

BACKGROUND: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare ...