Myocardial Infarction

Artificial intelligence approaches for non-invasive diabetes prediction using ECG signals: A systematic review.

Diabetes is a major global health challenge, with many individuals remaining undiagnosed due to the limitations of traditional screening methods. Artificial intelligence (AI)-based electrocardiogram (ECG) analysis offers a promising, non-invasive approach for the early detection of diabetes. This systematic review aims to critically evaluate machine learning (ML) and deep learning (DL) models deve...

Pilot clinical evaluation of artificial intelligence-driven guiding catheter simulation for optimizing percutaneous coronary intervention.

AIMS: In percutaneous coronary intervention (PCI), a suboptimal choice of guiding catheter may compromise coaxial alignment and backup support, prolonging procedures and increasing radiation and contrast exposure. We assessed whether a computed tomography (CT)-driven, artificial intelligence (AI)-guided preprocedural simulation could improve procedural efficiency and safety. METHODS AND RESULTS: I...

Validation and longitudinal trajectory analysis of an AI-based ECG model for aortic stenosis: from community screening to pre-TAVR risk stratification.

AIMS: Early aortic stenosis (AS) detection remains challenging, with many patients presenting late when left ventricular dysfunction may be irreversib...

Association and Agreement of Contact-Based Smartphone Photoplethysmography Compared With Electrocardiography: Protocol for a Systematic Review and Meta-Analysis.

BACKGROUND: Mobile health (mHealth), leveraging mobile devices for health measurement and promotion, is rapidly growing. Smartphone cameras can perfor...

FHGNet: A Feature-Centric Hierarchical Network with Graph Attention Layer for Supraventricular Tachycardia Classification.

Automated electrocardiogram (ECG) classification plays a critical role in arrhythmia diagnosis. However, current deep learning-based methodologies fre...

ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions.

Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threaten...

Study design for an emulated trial of a 2 arm, parallel, stratified, adaptive, RCT of CABG versus PCI in people requiring myocardial revascularization at high risk (High-Risk REVASC).

AIM: This study aims to use routinely collected health data and trial emulation methodology to inform the design of a pragmatic randomized controlled ...

Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.

AIMS: Periodic cardiac MRI (CMR) is recommended to identify adverse ventricular remodelling in repaired tetralogy of Fallot (TOF), but access to CMR i...

Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.

Accurate and timely stroke-risk prediction is necessary to help patients at risk take guided measures, as stroke remains a leading cause of death and ...

An Energy-Efficient ECG Classifier With On-Chip Learning Using Binarized Convolutional Neural Network.

In ECG classification applications, binarized convolutional neural networks (bCNNs) show great potential to achieve extremely low power consumption th...

Multisite derivation of a machine learning algorithm using high sensitivity troponin to predict major adverse cardiac events in the emergency department.

OBJECTIVE: To develop a machine learning (ML) algorithm to stratify risk for major adverse cardiac events (MACE) within 30 days in emergency departmen...

Prediction of mortality, bleeding, and ischaemic events in patients with cancer and acute coronary syndrome: a model development and validation study.

BACKGROUND: Accurate assessment of mortality, bleeding, and atherothrombotic risk in patients with cancer and acute coronary syndrome could inform nov...

A meta-heuristic aided arrhythmia classification model using advanced deep learning technique with multiple feature extraction mechanisms.

Cardiac arrhythmia poses an important threat to human life; hence it is an urge to diagnose properly. There are numerous mechanisms deployed for the i...

End-to-End Platform for Electrocardiogram Analysis and Model Fine-Tuning: Development and Validation Study.

BACKGROUND: Electrocardiogram (ECG) data constitutes one of the most widely available biosignal data in clinical and research settings, providing crit...

SLPM: a lightweight deep learning model for end-to-end paper ECG digitization.

OBJECTIVE: The digitization of paper electrocardiograms (ECGs) faces several challenges, including amplified errors during segmentation and signal ext...

Use of Electrocardiograms to Identify Coronary Artery Disease: Cross-Validation of an Artificial Intelligence Model.

BACKGROUND: The current gold standard for the diagnosis of coronary artery disease (CAD) is invasive angiography; however, it is an invasive procedure...

Machine learning models for predicting futile recanalisation after endovascular treatment in patients with large core infarction.

BACKGROUND: Predicting futile recanalisation following endovascular treatment (EVT) in patients with large core infarctions is crucial for guiding cli...

In-hospital outcomes of intravascular lithotripsy compared to rotational atherectomy and cutting/scoring balloon angioplasty.

BACKGROUND: Intravascular lithotripsy (IVL) emerged for the treatment of coronary artery calcification with encouraging safety and effectiveness rates...

[Wolff-Parkinson-White syndrome : Comparison of different algorithms].

BACKGROUND: Wolff-Parkinson-White (WPW) syndrome is characterised by accessory pathways that bypass the normal atrioventricular conduction system. Pre...

Foundation models for electrocardiogram interpretation: clinical implications.

BACKGROUND AND AIMS: The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solut...