Myocardial Infarction

Prediction of Left Atrial Volume Parameters from Resting ECGs and Tabular Data Using Deep Learning in the UK Biobank

We present a deep learning model that predicts left atrial (LA) volume from standard 12-lead ECG recordings and basic patient data. This approach offers a low-cost, scalable alternative to MRI-based LA volume measurement, which remains the clinical gold standard but is often inaccessible. Our model performs regression directly on LA volume targets and leverages Shapley values to provide interpreta...

Protocol for a prospective accuracy study on an artificial intelligence-based ultrasound system for gestational age estimation among pregnant women in Ghana, Kenya and South Africa

Background: Risk screening for pre-eclampsia relies on accurate gestational age assessment, but routine access to ultrasound-based gestational dating remains challenging in many low- and middle-income countries (LMICs). As part of the formative work for the Preventing pre-eclampsia: Evaluating AspiRin Low-dose regimens following risk Screening (PEARLS) trial, we aim to validate and implement an Ar...

Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment

Cardiac magnetic resonance imaging (CMR) offers detailed evaluation of cardiac structure and function, but its limited accessibility restricts use to ...

Contrastive Learning for Multi Label ECG Classification with Jaccard Score Based Sigmoid Loss

Recent advances in large language models (LLMs) have enabled the development of multimodal medical AI. While models such as MedGemini achieve high acc...

ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation

Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders...

Leveraging the wearable 1-lead ECG signal: From cardiac rhythm to cardiac function assessment

The electrocardiogram (ECG) is a critical tool in the diagnosis and monitoring of cardiovascular disease. Although traditional 12-lead ECGs offer comp...

Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography

Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregnancy management, optimized delivery planning, and ...

Long-term Cardiac Autonomic Effects of Prenatal Steroid Exposure: A Machine Learning Approach Integrating Heart Rate Variability and ECG Foundation Models

Background: Prenatal glucocorticoid administration is standard care for threatened preterm birth, but long-term cardiac autonomic effects remain incom...

Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation

Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopmental out...

AI-Enabled Privacy-Preserving Cardiac Diagnostics via Electrocardiograms

Electrocardiogram (ECG) is a widely available, non-invasive diagnostic tool used for cardiovascular screening and provides essential insights into hea...

Token Entropy Regularization for Multi-modal Antenna Affiliation Identification

Accurate antenna affiliation identification is crucial for optimizing and maintaining communication networks. Current practice, however, relies on the...

Token Entropy Regularization for Multi-modal Antenna Affiliation Identification

Accurate antenna affiliation identification is crucial for optimizing and maintaining communication networks. Current practice, however, relies on the...

UniPACT: A Multimodal Framework for Prognostic Question Answering on Raw ECG and Structured EHR

Accurate clinical prognosis requires synthesizing structured Electronic Health Records (EHRs) with real-time physiological signals like the Electrocar...

Development and Multinational Validation of Artificial Intelligence-Enabled ASCVD Risk Stratification Using Electrocardiograms

Aims: Despite the availability of clinical risk scores for atherosclerotic cardiovascular disease (ASCVD), their use is limited because the required p...

Deep Learning Decodes Latent ECG Signatures of Stress Cardiomyopathy

Background Stress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which may lead to misdiagnosis and misaligned management...

Transformer-based structuring of Italian electronic health records with application in cardiac settings

Purpose: Natural Language Processing (NLP) has the potential to extract structured clinical knowledge from unstructured Electronic Health Records (EHR...

Experience with Single Domain Generalization in Real World Medical Imaging Deployments

A desirable property of any deployed artificial intelligence is generalization across domains, i.e. data generation distribution under a specific acqu...

Time-series ECG Imputation Using a Pattern-Based Masking Framework

The utilization of continuous ECG monitoring has become an integral part of modern hospital-based care. However, missing data presents significant cha...

GARD: Genomic Data based Drug Repurposing in Head and Neck Cancer with Large Language Model Validation

Background/ObjectivesHead and neck cancer (HNC) represents the seventh most common cancer diagnosis globally, yet current treatments, including surger...

Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning

Atrial fibrillation (AF) is a common cardiac arrhythmia that significantly increases the risk of stroke and heart failure, necessitating reliable and ...