Arrhythmias

Nonperiodic dynamic CT reconstruction using backward-warping implicit neural representation with diffeomorphism regularization.

\textit{Objective.} Motion artifacts remain a major obstacle in dynamic computed tomography (CT) reconstruction, particularly for nonperiodic rapid motion such as cardiac imaging in patients with fast or irregular heart rates, where ECG-gated approaches are unreliable. The extreme limited-angle problem arising from insufficient angular coverage within a single cardiac phase makes accurate reconstr...

Scalable fabrication of gas sensors via spark-ablation printing of semiconductive metal oxide nanoparticles and heterostructures.

Semiconductive metal oxide (SMO) gas sensors are extensively used in air monitoring, industrial safety, and hazardous-gas detection due to their high sensitivity, low cost and low power consumption. Advances in nanotechnology have enabled precise control over the morphology and electronic structure of SMOs, thereby enhancing their sensing performances. However, the implementation of nanoscale SMOs...

Rhythms of mood: Bidirectional regulation of molecular clocks and neurotransmission in affective disorders.

Mood disorders, primarily major depressive and bipolar disorder, are characterized by significant neurochemical dysregulation and disturbances in biol...

Low-latency HRV analysis from ultra-short ECG windows using a modular deep-learning framework.

We present a universal modular deep-learning framework and demonstrate its application to low-latency, streaming-compatible heart rate variability (HR...

Three-dimensional visualization of arrhythmogenic substrate in mouse hearts using panoramic optical mapping and micro-computed tomography.

Electrical and structural remodeling of the heart can contribute to the development of cardiac arrhythmias. Ex vivo optical mapping has been used to v...

Personalized Deep Networks for Enhanced ECG Segmentation.

Electrocardiography (ECG) plays a vital role in the diagnosis of cardiovascular diseases by analyzing the electrical activity of the heart. ECG semant...

Telemedicine adoption in cardiology: Determinants and predictors identified using Bayesian Model Averaging and Machine Learning.

In this secondary analysis of a German cross-sectional survey data, we investigated key determinants and predictors of telemedicine (TM) use among hea...

Automatic segmentation of target volume of breast radiotherapy on CT using MSR-UNet: A multicenter study.

PURPOSE: We developed MSR-UNet(Mamba-based Skip Refinement U-Net) to accurately segment the clinical target volume (CTV) and tumor bed (TB) for breast...

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is wide...

AI-enhanced approaches for personalized cardiac treatment: insights from ECG data.

The analysis of drug-induced alterations in the electrocardiogram (ECG) is essential in measuring cardiac safety, but manual analysis is not always ac...

AI-enabled privacy-preserving cardiac diagnostics via electrocardiograms.

Electrocardiogram (ECG) is a widely available, non-invasive diagnostic tool used for cardiovascular screening and provides essential insights into hea...

Risk-Guided Atrial Fibrillation Screening With Artificial Intelligence-Enabled Electrocardiogram Models: A VITAL-AF Trial Analysis.

BACKGROUND: Screening for atrial fibrillation (AF) may lead to earlier detection and initiation of preventive measures. Current AF screening approache...

Hypotension Prediction Index-Guided Hemodynamic Management on Postoperative Organ Outcome: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.

Intraoperative hypotension (IOH) is a frequent occurrence during noncardiac surgery. Hypotensive episodes can compromise tissue perfusion, and cumulat...

Wearable ECG and PPG for anxiety detection: a translational digital medicine perspective.

Anxiety disorders affect hundreds of millions of people worldwide, yet objective and continuous assessment remains limited in clinical practice. To ou...

Wheelchair movement signal classification from EEG for motor-impaired individuals using novel deep learning architecture.

Purpose: Traditional wheelchair controls often limit independence and pose safety risks for motor-impaired users. To address these challenges, this st...

Differentiating long QT syndrome genotypes using electrocardiographic geometric parameterization and machine learning approaches.

Long QT Syndrome (LQTS) is an inherited cardiac disorder characterized by dysfunctional cardiac ion channels, which result in prolonged QT intervals o...

Applying machine learning and natural language processing to patient safety event reports: Identifying patterns of cardiovascular diagnostic errors.

OBJECTIVE: To explore the utility of natural language processing (NLP) and machine learning (ML) techniques to identify unsafe conditions leading to c...

Can an artificial intelligence electrocardiogram algorithm improve diagnostic accuracy for acute coronary occlusion in the difficult subset of canceled catheterization lab activations?

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) and its equivalents describe the electrocardiogram (ECG) findings of acute coronary occ...

Patient-specific instantaneous spatial temperature maps for MR-guided laser interstitial thermal therapy using a physics-assisted deep learning framework.

PURPOSE: Accurate prediction of the laser energy absorption and corresponding thermal spread is essential for safe and effective outcomes in magnetic ...

Beat-to-beat variability of ventricular repolarization reveals sex-specific instability and proarrhythmic risk.

Beat-to-beat QT interval variability (QTV) is a well-established marker of increased vulnerability to ventricular arrhythmias; however the underlying ...