AIMC Topic: Myocardial Infarction
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Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.
European heart journal
Jun 23, 2025
BACKGROUND AND AIMS: Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET f...
Integrating large language models with human expertise for disease detection in electronic health records.
Computers in biology and medicine
Jun 1, 2025
OBJECTIVE: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelli...
Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.
European heart journal
May 21, 2025
BACKGROUND AND AIMS: Emerging evidence supports artificial intelligence-enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of...
Optimized deep residual networks for early detection of myocardial infarction from ECG signals.
BMC cardiovascular disorders
May 17, 2025
Globally, the high number of deaths are happening due to Myocardial infarction (MI). MI is considered as a life-threatening disease, which leads to an increase number of deaths or damage to the heart, and hence, prompt detection of MI is critical to ...
An Interpretable Model for Predicting Acute Myocardial Infarction in Distinct Patient Profiles.
Studies in health technology and informatics
May 15, 2025
INTRODUCTION: Acute myocardial infarction (AMI) is highly prevalent (3.8% in developed countries), affecting heterogenous populations, and can be influenced by varied factors, including demographics, clinical risk factors, and comorbidities. Identify...
Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction.
BMC cardiovascular disorders
May 12, 2025
BACKGROUND: Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing ...
Large Language Models and Artificial Neural Networks for Assessing 1-Year Mortality in Patients With Myocardial Infarction: Analysis From the Medical Information Mart for Intensive Care IV (MIMIC-IV) Database.
Journal of medical Internet research
May 12, 2025
BACKGROUND: Accurate mortality risk prediction is crucial for effective cardiovascular risk management. Recent advancements in artificial intelligence (AI) have demonstrated potential in this specific medical field. Qwen-2 and Llama-3 are high-perfor...
Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning.
BMC neurology
May 8, 2025
BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability o...
Predicting In-Hospital Mortality in Patients With Acute Myocardial Infarction: A Comparison of Machine Learning Approaches.
Clinical cardiology
Apr 1, 2025
BACKGROUND: Acute myocardial infarction (AMI) remains a leading global cause of mortality. This study explores predictors of in-hospital mortality among AMI patients using advanced machine learning (ML) techniques.
Clinical decision support using machine learning and cardiac troponin for the diagnosis of myocardial infarction.
European heart journal. Acute cardiovascular care
Aug 28, 2024
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