Arrhythmias

Artificial Intelligence in detection of acute coronary occlusion in NSTEMI patients.

The prognosis of patients with MI has improved significantly with the recognition that early reperfusion is critical, particularly since timely percutaneous coronary intervention (PCI) became widely adopted. The invasive reperfusion era also reshaped MI diagnostics, shifting the paradigm from Q-wave vs. Non-Q-wave MI to ST-Elevation Myocardial Infarction (STEMI) vs. Non-ST-Elevation Myocardial Inf...

Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms.

AIMS: The prone electrocardiogram (ECG) presents challenges in detecting anterior ST-segment elevated myocardial infarction (STEMI). This study aims to develop a method to convert prone ECGs to standard ECGs to facilitate physician diagnosis of STEMI and other cardiovascular diseases (CVD). METHODS AND RESULTS: The standard ECGs, vectorcardiograms (VCGs), and prone ECGs were prospectively examined...

Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia affecting over 50 million individuals worldwide, with serious complications including strok...

Arrhythmia risk predictions from molecular simulations of cardiac ion channel-drug interactions.

Unintended block of cardiac ion channels, particularly hERG (KV11.1), remains a key concern in drug development as disruption of ion channel function ...

Reduced left atrial strain from 4-dimensional computed tomography predicts atrial fibrillation recurrence after ablation.

BACKGROUND: Existing models that use clinical history and cardiac imaging data remain inadequate for accurate prediction of the success of catheter ab...

Application value of prostate-specific antigen density combined with multiparametric MRI in early diagnosis of prostate cancer.

BACKGROUND: Diagnosis of prostate cancer in the PSA gray zone (4-10 ng/mL) and PI-RADS 3 cases remains challenging. Although multiparametric MRI (mpMR...

Artificial intelligence-enabled electrocardiographic sex discordance and the risk of incident atrial fibrillation: A multinational cohort study.

BACKGROUND: Binary classification of sex fails to capture the sex-related continuum of atrial fibrillation (AF) risk. OBJECTIVE: This study aimed to d...

Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram.

INTRODUCTION: Transthoracic echocardiography (TTE) is the current standard for detecting tricuspid regurgitation (TR); however, it incurs additional c...

An artificial intelligence model for electrocardiogram detection of occlusion myocardial infarction: a retrospective study to reduce false-positive cath lab activations.

AIMS: Existing ST-segment elevation myocardial infarction (STEMI) alert pathways that rely on traditional STEMI criteria perform suboptimally. We aime...

Susceptibility of Large Language Models to User-Driven Factors in Medical Queries.

UNLABELLED: Large language models (LLMs) are increasingly used in healthcare; however, their reliability is shaped not only by model design but also b...

Artificial intelligence-predicted ECG age gap as a biomarker: bias-adjusted correlation with mortality and cardiovascular risk factors.

AIMS: Artificial intelligence models can estimate a person's age from ECG. The gap between the predicted ECG age and chronological age, predicted age ...

Deep Learning Model for High-Accuracy Classification of Premature Ventricular Contractions With Precordial Transition Zones in Leads V3 or V4.

BACKGROUND: Predicting the origin of premature ventricular contractions (PVCs) is challenging when a transition zone (TZ) appears in leads V3 and V4. ...

Spectral anomaly detection in physiological time-series data: A systematic review.

BACKGROUND: The identification of anomalies in physiological time-series data, specifically ECG and EEG spectra, is a key part of the diagnostic proce...

Sleep stage classification from ECG using machine learning: Evaluating the impact of signal duration.

Sleep stage flagging is critical for diagnosing conditions like insomnia, sleep apnea, and narcolepsy. Traditional methods rely on time-intensive manu...

Agreement of the performance of equine electrocardiogram recording devices for ECG complexity analysis.

BACKGROUND: Non-linear equine electrocardiography (ECG) analysis is an actively developing study area which has the potential to lead to novel, artifi...

Development of a predictive model for eosinophilia in patients treated with ampicillin/sulbactam or piperacillin/tazobactam.

AIM: We aim to identify risk factors for antibiotic-induced eosinophilia in hospitalized patients receiving penicillin/beta-lactamase inhibitor therap...

Targetable atrial tachycardias after artificial intelligence-guided ablation of persistent atrial fibrillation.

BACKGROUND: The TAILORED-AF randomized trial demonstrated that artificial intelligence-guided ablation of spatiotemporal dispersion in addition to pul...

Intelligent ensemble learning-enhanced finite element modeling for precision thermal ablation in cancer therapy.

BACKGROUND: Microwave ablation (MWA) is a minimally invasive treatment for liver tumors, yet accurate prediction of ablation zones remains challenging...

Deep learning for automatic detection of hepatocellular carcinoma in dynamic contrast-enhanced MRI.

OBJECTIVE: Develop a deep learning model for automatic hepatocellular carcinoma (HCC) detection in T1 weighted imaging (WI) Dynamic Contrast-Enhanced ...

Deep learning algorithm for detection of acute heart failure using standard ECG waveforms.

AIMS: To develop and evaluate a deep learning model for immediate and accurate diagnosis of acute heart failure(HF) using standard 12-lead electrocard...