Myocardial Infarction

Inflammatory Biomarkers & Interpretable ML for SAP Risk Stratification in AIS Patients Undergoing Bridging Therapy

Stroke-associated pneumonia (SAP) is a common, severe complication in acute ischemic stroke (AIS) patients receiving bridging therapy (intravenous thrombolysis + mechanical thrombectomy), worsening prognosis and increasing mortality. Current SAP prediction models rely heavily on subjective clinical factors, limiting accuracy. This study developed an interpretable machine learning (ML) model combin...

CARE-ECG: Causal Agent-based Reasoning for Explainable and Counterfactual ECG Interpretation

Large language models (LLMs) enable waveform-to-text ECG interpretation and interactive clinical questioning, yet most ECG-LLM systems still rely on weak signal-text alignment and retrieval without explicit physiological or causal structure. This limits grounding, temporal reasoning, and counterfactual "what-if" analysis central to clinical decision-making. We propose CARE-ECG, a causally structur...

Frailty Estimation in Elderly Oncology Patients Using Multimodal Wearable Data and Multi-Instance Learning

Frailty and functional decline strongly influence treatment tolerance and outcomes in older patients with cancer, yet assessment is typically limited ...

Stress Estimation in Elderly Oncology Patients Using Visual Wearable Representations and Multi-Instance Learning

Psychological stress is clinically relevant in cardio-oncology, yet it is typically assessed only through patient-reported outcome measures (PROMs) an...

Cardiovascular Adverse Events After Definitive Chemoradiotherapy for Lung Cancer in an Appalachian Population: Incidence and Machine Learning Based Prediction

Background Cardiovascular adverse events (CVAEs) after chemoradiotherapy (CRT) for lung cancer are major concerns in Appalachia due to high rates of s...

Learning ECG Image Representations via Dual Physiological-Aware Alignments

Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appear...

Bit-Identical Medical Deep Learning via Structured Orthogonal Initialization

Deep learning training is non-deterministic: identical code with different random seeds produces models that agree on aggregate metrics but disagree o...

Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework

Low left ventricular ejection fraction (LEF) frequently remains undetected until progression to symptomatic heart failure, underscoring the need for s...

Foundation Model for Cardiac Time Series via Masked Latent Attention

Electrocardiograms (ECGs) are among the most widely available clinical signals and play a central role in cardiovascular diagnosis. While recent found...

ECG spectrogram-based deep learning model to predict deterioration of patients with early sepsis at the emergency department: a study from the Acutelines data- and biobank

Purpose: Early recognition of deterioration in patients with suspected infection at the emergency department (ED) is important. Current clinical scori...

Performance Assessment of ECG Delineators on Single-Lead Wearable Ambulatory Data

Reliable interpretation of electrocardiograms (ECGs) requires precise identification of P, QRS, and T (PQRST) wave boundaries. However, it remains cha...

AI-Derived ECG Age Gap as a Digital Biomarker for Cardiovascular Risk Stratification

Cardiovascular diseases remain the leading cause of global mortality, and early risk stratification is critical for improving prognosis. Artificial in...

Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis

Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts...

Clinical Research Collaboration for Stroke in Korea Imaging Repository:A Prospective Multicenter Neuroimaging Repository

Background: Prospective stroke registries have advanced our understanding of cerebrovascular disease, yet most reduce neuroimaging to categorical vari...

Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping

Sleep disturbances are tightly linked to cardiovascular risk, yet polysomnography (PSG)-the clinical reference standard-remains resource-intensive and...

CARDIAC-FM: A Multimodal Foundation Model for Cardiovascular Risk Prediction Using ECG and Cardiac MRI

Atrial fibrillation and heart failure impose substantial health burdens worldwide, yet existing prediction models lack sufficient accuracy and general...

Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction

Reconstructing a 12-lead electrocardiogram (ECG) from a reduced lead set is an ill-posed inverse problem due to anatomical variability. Standard deep ...

Vib2ECG: A Paired Chest-Lead SCG-ECG Dataset and Benchmark for ECG Reconstruction

Twelve-lead electrocardiography (ECG) is essential for cardiovascular diagnosis, but its long-term acquisition in daily life is constrained by complex...

Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment

Hyperkalemia is a life-threatening electrolyte disorder that is common in patients with chronic kidney disease and heart failure, yet frequent monitor...

ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation

While Multimodal Large Language Models (MLLMs) show promising performance in automated electrocardiogram interpretation, it remains unclear whether th...