Diagnostic performance of single-lead electrocardiograms from a smartwatch and a smartring for cardiac arrhythmia detection.

Background: Wearable devices are widely used for atrial fibrillation (AF) detection, yet most validation studies include only sinus rhythm or AF, likely overestimating diagnostic performance.

Objective: This multicenter study assessed the performance of automated AF detection and physician interpretation of single-lead electrocardiograms (SL-ECGs) from the Apple Watch and CART Ring.

Methodology: Participants underwent simultaneous 12-lead ECG and SL-ECGs from Apple Watch and CART Ring.

3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.

Cardiac anatomy and physiology vary considerably across the human population. Understanding and taking into account this variability is crucial for both accurate clinical decision-making and realistic in silico modeling of cardiac function. In this work, we propose multi-class variational point cloud autoencoders (Point VAE) as a novel geometric deep learning approach for 3D cardiac shape and function analysis.

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.

Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We introduce a novel methodology for predicting HF risk using 12-lead electrocardiograms (ECGs).

Deep conditional generative model for personalization of 12-lead electrocardiograms and cardiovascular risk prediction.

Background: 12-lead electrocardiograms (ECGs) are a cornerstone for diagnosing and monitoring cardiovascular diseases (CVDs). They play a key role in detecting abnormalities such as arrhythmias and myocardial infarction, enabling early intervention and risk stratification. However, traditional analysis relies heavily on manual interpretation, which is time-consuming and expertise-dependent.

Real-time smartphone alerts during atrial fibrillation episodes with implantable cardiac monitors and wearable devices: SMART-ALERT study.

Background: Atrial fibrillation (AF) is a major cause of stroke, with dynamic thromboembolic risk during and shortly after episodes. Implantable cardiac monitors and wearable devices have emerged as tools for real-time AF detection, yet their comparative performance in supporting anticoagulation strategies remains underexplored.

Objective: This single-center feasibility study investigated the performance of real-time AF detection and notification for episodes lasting longer than 30 minutes.

Identifying extra pulmonary vein targets for persistent atrial fibrillation ablation: bridging advanced and conventional mapping techniques.

Aims: Advanced technologies such as charge density mapping (CDM) show promise in guiding adjuvant ablation in patients with persistent atrial fibrillation (AF); however, their limited availability restricts widespread adoption. We sought to determine whether regions of the left atrium containing CDM-identified pivoting and rotational propagation patterns during AF could also be reliably identified using more conventional contact mapping techniques.

Methods And Results: Twenty-two patients undergoing de novo ablation of persistent AF underwent both CDM and electroanatomic voltage mapping during AF and sinus rhythm with multiple pacing protocols.

Deep learning based coronary vessels segmentation in X-ray angiography using temporal information.

Invasive coronary angiography (ICA) is the gold standard imaging modality during cardiac interventions. Accurate segmentation of coronary vessels in ICA is required for aiding diagnosis and creating treatment plans. Current automated algorithms for vessel segmentation face task-specific challenges, including motion artifacts and unevenly distributed contrast, as well as the general challenge inherent to X-ray imaging, which is the presence of shadows from overlapping organs in the background.

Personalized topology-informed localization of standard 12-lead ECG electrode placement from incomplete cardiac MRIs for efficient cardiac digital twins.

Cardiac digital twins (CDTs) offer personalized in-silico cardiac representations for the inference of multi-scale properties tied to cardiac mechanisms. The creation of CDTs requires precise information about the electrode position on the torso, especially for the personalized electrocardiogram (ECG) calibration. However, current studies commonly rely on additional acquisition of torso imaging and manual/semi-automatic methods for ECG electrode localization.

Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.

Atrial fibrillation (AF) is the most prevalent clinical arrhythmia, posing significant mortality and morbidity challenges. Outcomes of current catheter ablation treatment strategies are suboptimal, highlighting the need for innovative approaches. A major obstacle lies in the inability to comprehensively assess both structural and functional remodelling in AF.

Computational pathology applied to clinical colorectal cancer cohorts identifies immune and endothelial cell spatial patterns predictive of outcome.

Colorectal cancer (CRC) is a histologically heterogeneous disease with variable clinical outcome. The role the tumour microenvironment (TME) plays in determining tumour progression is complex and not fully understood. To improve our understanding, it is critical that the TME is studied systematically within clinically annotated patient cohorts with long-term follow-up.

NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation.

Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes.

"Real-world" performance of the Confirm Rx™ SharpSense AF detection algorithm: UK Confirm Rx study.

Introduction: The novel Confirm Rx™ implantable cardiac monitor (ICM) with SharpSense™ technology incorporates a new P-wave discriminator designed to improve AF detection. This study aimed to evaluate the diagnostic performance of the Confirm Rx™ ICM in detecting AF episodes of varying durations.

Methods: We conducted a multicenter retrospective analysis of consecutive patients implanted with a Confirm Rx™ ICM (v1.

Modeling the Mechanisms of Non-Neurogenic Dynamic Cerebral Autoregulation.

Objective: Dynamic cerebral autoregulation (dCA) refers to a collection of mechanisms that act to maintain steady state cerebral blood flow (CBF) near constant despite changes in arterial blood pressure (ABP), but which is known to become impaired in various cerebrovascular diseases. Currently, the mechanisms of dCA and how they are affected in different physiological conditions are poorly understood. The objective of this study was to disentangle the magnitudes and time scales of the myogenic and metabolic responses of dCA, in order to investigate how each mechanism is affected in impaired dCA.

Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia associated with significant morbidity and mortality. Managing risk of stroke and AF burden are pillars of AF management. Atrial geometry has long been recognized as a useful measure in achieving these goals.

Role of impedance drop and lesion size index (LSI) to guide catheter ablation for atrial fibrillation.

Background: When using lesion size index (LSI) to guide catheter ablation, it is unclear what combination of power, contact force and time would be preferable to use and what LSI target value to aim for. This study aimed at identifying desirable ablation settings and LSI targets by using tissue impedance drop as indicator of lesion formation.

Methods: Consecutive patients, undergoing their first left atrial (LA) catheter ablation for atrial fibrillation, with radiofrequency energy (RF) powers of 20, 30 and 40 W were enrolled.

Patient-specific 3D coronary model in cardiac catheterisation laboratories.

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions.

Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations.

Modeling 3D Cardiac Contraction and Relaxation With Point Cloud Deformation Networks.

Global single-valued biomarkers, such as ejection fraction, are widely used in clinical practice to assess cardiac function. However, they only approximate the heart's true 3D deformation process, thus limiting diagnostic accuracy and the understanding of cardiac mechanics. Metrics based on 3D shape have been proposed to alleviate these shortcomings.

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform.

3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks.

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases with associated clinical decision-making typically based on single-valued imaging biomarkers. However, such metrics only approximate the complex 3D structure and physiology of the heart and hence hinder a better understanding and prediction of MI outcomes. In this work, we investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events.

Multi-class point cloud completion networks for 3D cardiac anatomy reconstruction from cine magnetic resonance images.

Cine magnetic resonance imaging (MRI) is the current gold standard for the assessment of cardiac anatomy and function. However, it typically only acquires a set of two-dimensional (2D) slices of the underlying three-dimensional (3D) anatomy of the heart, thus limiting the understanding and analysis of both healthy and pathological cardiac morphology and physiology. In this paper, we propose a novel fully automatic surface reconstruction pipeline capable of reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI acquisitions.