Myocardial Infarction

Association between electrocardiographic age and cognitive function: findings from the UK biobank and Framingham Heart Study.

AIMS: Biological age derived from 12-lead electrocardiograms (ECGs) using deep learning has emerged as a promising marker of physiological ageing. However, its relationship with cognitive performance remains poorly understood. To investigate the association between ECG-derived ageing and cognitive performance in two large population-based cohorts. METHODS AND RESULTS: We analysed data from the UK ...

Artificial intelligence-based automated interpretation of images of electrocardiograms: development and multinational validation of ECG-GPT.

AIMS: Timely, accurate assessment of electrocardiograms (ECGs) is crucial for diagnosing, triaging, and managing patients. However, this often relies on expert interpretation, a major bottleneck in low-resource settings. We developed and validated ECG-GPT, a format-independent vision encoder-decoder model that generates expert-level interpretations from 12-lead ECG images. METHODS AND RESULTS: We ...

Morphology-adaptive Au-Ag nanowire elastronics for integrated FlexoSERS and bioelectrical sensing.

We introduce a morphology-adaptive Au-Ag nanowire elastronic platform that conforms to diverse geometries while enabling multimodal optical-electrical...

Prediction of hemorrhagic transformation after intravenous thrombolysis in acute anterior circulation ischemic stroke through radiomics-based machine learning models.

BACKGROUND: Hemorrhagic transformation (HT) and its associated neurological deterioration remain major concerns that limit decision-making in thrombol...

Adaptive Autocorrelation Based Heart Rate Estimation from Single-Axis Seismocardiogram: A Comprehensive Benchmark Across Six Diverse Datasets.

We introduce AACFD (Adaptive Autocorrelation Function Detector), a lightweight, fully automatic pipeline for estimating window-averaged heart rate (HR...

APOE genotype and the effect of statins on lipid outcomes: A meta-analysis.

AIM: APOE genotype may affect statin therapy response. We conducted a meta-analysis to update and quantify this association across various outcomes. M...

Development and validation of predictive models for prognostic assessment in patients with acute non-ST-segment elevation myocardial infarction: a multicentre real-world study from China.

BACKGROUND: Few models specifically predict the prognoses of non-ST-segment elevation myocardial infarction (NSTEMI) patients. Additionally, these mod...

Multistage Electrocardiographic Profiling for Mental Stress-Induced Myocardial Ischemia in Women: A Prospective, Age-Matched Cohort Study in China.

BACKGROUND: Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in wo...

Detection and prognostic stratification of left ventricular systolic dysfunction in left bundle branch block using an artificial intelligence-enabled electrocardiography.

BACKGROUND: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchro...

Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis.

Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification re...

Interpretable multimodal machine learning model for predicting health risks of patients with heart failure.

Heart failure (HF) is one of the major causes of morbidity and mortality globally, necessitating accurate tools for health outcome prediction and risk...

A Machine Learning-Based Prognostic Model for Sepsis-Associated Liver Injury Using Routine Indicators.

OBJECTIVE: Sepsis-associated liver injury (SALI) occurs in approximately 40% of sepsis cases and is linked to high mortality, a challenge that may ste...

Development and explanation of electrocardiogram-based deep learning for predicting short-term mortality in heart failure patients.

BACKGROUND: Heart failure mortality has risen sharply after years of decline, highlighting the limitations of current risk assessment tools in accurac...

A lightweight residual dilated temporal transformer block for ECG classification on edge devices.

Wearable devices play a crucial role in healthcare by enabling continuous monitoring of vital physiological signals such as ECG, heart rate, respirati...

Toward practical screening of mortality risk: Insights from interpretable machine learning in NHANES.

BACKGROUND: Efficient community-based screening for individuals at high risk of mortality is a major public health challenge. While many predictors ha...

Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery.

AIMS: The role of electrocardiography (ECG) has been limited in the preoperative risk evaluation in noncardiac surgery due to its low prognostic value...

Development and validation of a machine learning model for prediction of 1-year mortality following ST-elevation myocardial infarction: a retrospective cohort study.

OBJECTIVES: To develop a machine learning (ML)-based risk prediction model for 1-year mortality in ST-elevation myocardial infarction (STEMI) patients...

Diagnostic model and identification of IGFBP2 as an aging-related biomarker and therapeutic target for systemic sclerosis-associated interstitial lung disease through integrated bioinformatics and machine learning approaches.

BACKGROUND: Cellular senescence is a critical contributor to the pathogenesis of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Ho...

Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.

AIMS: Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (A...

Can atrial fibrillation ablation outcomes be properly predicted with electrocardiography and artificial intelligence?

AIMS: The success of ablation for atrial fibrillation (AF) varies, often leading to repeat ablation. Reliable prediction of repeat ablation remains ch...