Myocardial Infarction

Multimodal deep learning for acute myocardial infarction detection from 12-lead electrocardiogram: a multi-centre study with cross-hospital validation.

AIMS: Acute myocardial infarction (AMI) remains a leading global cause of mortality, where timely diagnosis is critical to enable early intervention. The 12-lead electrocardiogram (ECG) is a critical tool for AMI detection. While deep learning (DL) models show promise for automated ECG analysis, most prior studies rely on small, curated datasets with limited external validation, limiting their cli...

Multi-modal deep-learning troponin prediction from electrocardiograms and demographic data.

AIMS: Electrocardiograms (ECGs) and troponin (Tn) testing are essential tools for the diagnosis and management of cardiac conditions. Prompt diagnosis using these tools can significantly improve patient outcomes. METHODS AND RESULTS: The objective of this study was to design and create a deep-learning model capable of predicting high-sensitivity troponin (hs-Tn) elevation in patients undergoing ch...

Machine learning-enabled assessment of risk of drug-induced QT prolongation at the time of prescribing.

BACKGROUND: Many medications are associated with long QTc. Current long QTc predictors have limited generalizability and/or modest performance. OBJECT...

Real-Time Phase-Contrast Cardiovascular MRI Using a Deep Learning Reconstruction Network With Combined Dictionary Learning and CNN.

PURPOSE: This study aims to develop real-time phase-contrast (PC) cardiovascular MRI with low latency. METHODS: In this study, a framework using golde...

Sleep disturbance recorded via wearable sensors predicts depression severity 9 years later.

BACKGROUND: Major depressive disorder (MDD) is prevalent and poses major public health implications. Autonomic nervous system (ANS) dysregulation and ...

Feature extraction and intelligent diagnosis of ECG signals based on KANs and xLSTM.

Cardiovascular disease (CVD) is the top cause of mortality globally, making it crucial to diagnose arrhythmias promptly and accurately for the early p...

Image based artificial intelligence-enhanced electrocardiogram prediction of incident atrial fibrillation.

BACKGROUND: Early prediction of atrial fibrillation (AF) is crucial for reducing adverse outcomes. While artificial intelligence-enhanced electrocardi...

Newton-Puiseux analysis for interpretability and calibration of complex-valued neural networks.

Complex-valued neural networks (CVNNs) are particularly suitable for handling phase-sensitive signals, including electrocardiography (ECG), radar/sona...

GDT-Net: Multi-level feature extraction network for precise diagnosis of atrial and ventricular fibrillation.

Atrial fibrillation (AFIB) and ventricular fibrillation (VFIB) are two critical cardiovascular diseases, where accurate diagnosis is essential for tim...

Artificial Intelligence-Based Algorithms Improve Care of Patients with AAA.

BACKGROUND: Timely detection and monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent ruptures and decrease mortality. Artificial ...

GAN-based novel feature selection approach with hybrid deep learning for heartbeat classification from ECG signal.

Heart arrhythmias are one of the most important categories of cardiovascular illness. A heartbeat that is abnormal like too early, too slow, too fast,...

Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks...

Driver Emotion Recognition Using Multimodal Signals by Combining Conformer and Autoformer.

This study aims to develop a multimodal driver emotion recognition system that accurately identifies a driver's emotional state during the driving pro...

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.

INTRODUCTION: Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet thera...

Integrating generative AI in perinatology: applications for literature review.

Perinatology relies on continuous engagement with an expanding body of clinical literature, yet the volume and velocity of publications increasingly e...

A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.

Current machine learning-based (ML) models usually attempt to utilize all available patient data to predict patient outcomes while ignoring the associ...

Latent Class Analysis Identifies Distinct Patient Phenotypes Associated With Mistaken Treatment Decisions and Adverse Outcomes in Coronary Artery Disease.

This study aimed to identify patient characteristics linked to mistaken treatments and major adverse cardiovascular events (MACE) in percutaneous coro...

Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.

BACKGROUND: Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI succes...

ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals.

. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods.. This study prop...

Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.

This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP)...