Arrhythmias

The potential for large language models to transform cardiovascular medicine.

Cardiovascular diseases persist as the leading cause of death globally and their early detection and prediction remain a major challenge. Artificial intelligence (AI) tools can help meet this challenge as they have considerable potential for early diagnosis and prediction of occurrence of these diseases. Deep neural networks can improve the accuracy of medical image interpretation and their output...

Model based deep learning method for focused ultrasound pathway scanning.

The primary purpose of high-intensity focused ultrasound (HIFU), a non-invasive medical therapy, is to precisely target and ablate tumors by focusing high-frequency ultrasound from an external power source. A series of ablations must be performed in order to treat a big volume of tumors, as a single ablation can only remove a small amount of tissue. To maximize therapeutic efficacy while minimizin...

Machine Learning Based on Clinical Information and Integrated CT Radiomics to Predict Local Recurrence of Stage Ia Lung Adenocarcinoma after Microwave Ablation.

PURPOSE: To develop and compare 3 different machine learning-based models of clinical information and integrated radiomics features predicting the loc...

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treati...

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and be...

A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers.

This study addresses the critical issue of drug-induced torsades de pointes (TdP) risk assessment, a vital aspect of new drug development due to its a...

Feasibility of Artificial Intelligence Powered Adverse Event Analysis: Using a Large Language Model to Analyze Microwave Ablation Malfunction Data.

Determine if a large language model (LLM, GPT-4) can label and consolidate and analyze interventional radiology (IR) microwave ablation device safety...

Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhyt...

A prognostic model for thermal ablation of benign thyroid nodules based on interpretable machine learning.

INTRODUCTION: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-gui...

AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations.

BACKGROUND: Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to a higher risk of mortality and morbidity. To predi...

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.

BACKGROUND: Researchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. ...

The influence of mental calculations on brain regions and heart rates.

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroence...

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-su...

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defined diagnostic criteria and advanced drug developme...

Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artif...

Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.

The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have been shown to have the identify features not immediat...

Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant proportion of patients still experience recurrence...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective atten...

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis o...

MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient too...