Arrhythmias

Improving arrhythmic risk prediction using cardiac magnetic resonance within deep learning in ischemic heart disease.

Sudden arrhythmic death remains a major clinical risk in ischemic heart disease (IHD), underscoring the need for improved risk stratification. Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) provides measures of scar burden and heterogeneity, but its incremental prognostic value beyond conventional markers such as left ventricular ejection fraction remains uncertain. We analysed t...

Precise ECG diagnosis and validation of educational utility for acute myocardial infarction using deep learning and explainable artificial intelligence.

Artificial intelligence (AI) holds significant promise for electrocardiogram (ECG) analysis, yet accurately detecting non-ST-segment elevation myocardial infarction (NSTEMI) and overcoming the "black box" nature of deep learning models remain persistent challenges. Here, we present a comprehensive deep learning framework capable of classifying STEMI, NSTEMI, and non-acute coronary syndrome (non-AC...

Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study.

BACKGROUND: High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly depend...

Preictal reduction in heart rate variability entropy is associated with functional/dissociative seizures and provides modest discrimination from epileptic seizures.

OBJECTIVE: Differentiating functional/dissociative seizures (FDS) from epileptic seizures (ES) remains clinically challenging, with limited electrocar...

ChemoCardioNet: An Explainable Multimodal Transformer for Early Prediction of Chemotherapy-Induced Cardiotoxicity.

Chemotherapy-induced cardiotoxicity (CIC) remains a major cause of morbidity and mortality among cancer survivors, and conventional monitoring often f...

Dynamic Beat-to-Beat Blood Pressure Estimation using a Multi-modal Wearable Deep Learning Approach.

Cuffless blood pressure (BP) monitoring technologies, primarily based on pulse transit time (PTT) or photoplethysmography (PPG), frequently suffer fro...

Endomyocardial Gremlin-1 is associated with structural remodeling and adverse clinical outcomes in non-ischemic cardiomyopathy.

BACKGROUND: Risk stratification in non-ischemic cardiomyopathies (NICM) remains challenging despite guideline-based phenotypic classification using mu...

Explainable ECG Analysis by Explicit Information Disentanglement With VAEs.

OBJECTIVE: The interpretation of electrocardiogram (ECG) signals is vital for diagnosis of cardiac conditions. Traditional methods rely on expert know...

[The application of artificial intelligence in arrhythmology].

In recent decades, clinical practice has been founded on the principles of evidence-based medicine, where therapeutic decisions arise from the integra...

In-Hospital Cardiac Arrest Detection Performance Analysis and Comparison on Effective Feature Selection.

BACKGROUND: How to reduce the occurrence of in-hospital cardiac arrest (IHCA), screen potential IHCA patients, and advance the treatment of IHCA are u...

Artificial intelligence-based automated sleep staging using heart rate variability: Assessment of performance and clinical prospects.

Background Some artificial intelligence models use heart rate variability (HRV) features to classify sleep stages. Estimation of HRV indices requires ...

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Microwave ablation is a crucial option for liver tumors, with success hinging on generating a suitably sized ablation zone for complete tumor eradicat...

Single-Cell Transcriptomics and Mendelian Randomization Analysis Reveal Key Genes in Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. It reduces quality of life and increases the risk of complications...

Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.

BACKGROUND: Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, affects 2% to 4% of the global adult population and is associated with an...

Noninvasive and minimally invasive approaches for acute and chronic stress assessment in dairy cattle.

In modern dairy production, cattle are routinely exposed to a wide range of management-related, environmental, and biological stressors all of which c...

AI-driven model for early failure prediction after HIFU integrating immediate post-ablation ultrasound and clinical data.

PURPOSE: High-Intensity Focused Ultrasound (HIFU) is an emerging focal therapy for localized prostate cancer, offering an alternative to radical prost...

Deep learning analysis of ECGs detects Cardiovascular-Kidney-Metabolic syndrome burden in people with diabetes: a report from the Silesia Diabetes-Heart Project.

BACKGROUND: Cardiovascular-kidney-metabolic (CKM) syndrome refers to the co-occurrence of obesity, diabetes, chronic kidney disease (CKD), and cardiov...

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysom...

An attention-guided multimodal deep learning framework by integrating CT-PET imaging and clinical data for lung cancer detection.

The proposed multi-modal deep learning system for lung cancer diagnosis and characterisation uses structural (CT), functional (PET), and clinical (EHR...

Deep learning analysis of 12-lead electrocardiograms for bloodstream infection prediction: a multi-center validation study.

BACKGROUND: Prompt diagnosis of bloodstream infections (BSIs) is critical for antimicrobial stewardship but hindered by blood culture delays of 48 h o...