Arrhythmias

[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 integration of clinical expertise, patient preferences, and scientific evidence derived from controlled studies and meta-analyses. The advent of artificial intelligence (AI) in health care, however, is driving a significant evolution in clinical research, o...

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 urgent problems to be solved in clinic. In this study, we tried to develop a model to predict whether patients will develop IHCA based on the data of patients who have just been admitted to hospital and evaluate the influence of different feature sele...

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 ...

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...

Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.

BACKGROUND: Ovarian cancer patients requiring intensive care unit (ICU) admission face particularly grave prognosis, yet current prognostic models rel...

Tumor ablation: emerging uses, challenges, and strategic implementation. A green paper by the Network of Expertise in Cancer (JANE-2), High Tech Medical Resources, network on Physical Methods of Tumor Ablation.

The field of oncology has witnessed remarkable progress with the integration of high-tech innovations in tumor ablation. Tumor ablation therapies, suc...

Hybrid deep learning for mental workload classification using EEG with enhanced preprocessing and interpretability.

Mental workload classification is critical in safety-sensitive fields such as healthcare and aviation. However, electroencephalography-based approache...

Rethinking prediction of sudden cardiac arrest: The role of electrocardiography in forecasting low-incidence, high-consequence events.

Sudden cardiac arrest (SCA) remains a leading cause of mortality, accounting for 300,000-400,000 deaths annually in the United States. Despite advance...

Serial AI-Enabled Electrocardiogram Trajectories During Heart Failure Treatment: A Proof-of-Concept Case Series.

BACKGROUND: Artificial intelligence-enabled electrocardiogram (AI-ECG) detects left ventricular systolic and diastolic dysfunction at single time poin...

Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model.

OBJECTIVE: 30-day survival after cardiac arrest is low, 12.4% and 36% for out-of-hospital and in-hospital cardiac arrest, respectively. Heart failure ...

Edge-AI enabled secure IoT framework for real-time patient monitoring and anomaly detection in smart healthcare systems.

Hospitals desire knowledge of bedside sensors in real time but they do not wish to send everything to the cloud. We propose an Edge-AI framework used ...

Advancing cardiac health assessment using deep convolutional neural networks for ECG image analysis.

This research introduces a novel technique for early prediction of cardiac affliction in ECG imagery. The initial phase involves pre-processing using ...

Cardiac substructure dosimetry in thoracic radiotherapy: moving beyond mean heart dose and toward precision cardiac risk mitigation.

BACKGROUND: Radiation-induced heart disease (RIHD) remains a clinically significant consequence of thoracic radiotherapy (RT). Historically, the mean ...