Clinical Updates and Future Perspectives on Acute Myocardial Infarction

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 865

Special Issue Editors


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Guest Editor
Division of Cardiology, “A. Cardarelli” Hospital, Via A. Cardarelli, 6, 80131 Naples, Italy
Interests: heart attack; atrial fibrillation; angioplasty; coronary angiography

E-Mail Website
Guest Editor
Division of Cardiology, “A. Cardarelli” Hospital, Via A. Cardarelli, 6, 80131 Naples, Italy
Interests: echocardiography; electrophysiology; interventional cardiology; chronic coronary syndrome; myocardial infarction; arrhythmias; heart failure; infective endocarditis

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Guest Editor Assistant
Division of Cardiology, “A. Cardarelli” Hospital, Via A. Cardarelli, 6, 80131 Naples, Italy
Interests: coronary angiography; hypertension; PCI; atherosclerosis; myocardial infarction; acute myocardial infarction; chronic coronary syndrome; diabetology; peripheral atherosclerotic disease

Special Issue Information

Dear Colleagues,

This Special Issue of Medicina, titled "Clinical Updates and Future Perspectives on Acute Myocardial Infarction", explores the latest advancements in both the acute management and chronic care of acute myocardial infarction (AMI). Despite significant progress in diagnosis and treatment, AMI remains a leading cause of morbidity and mortality, requiring continuous innovation in reperfusion strategies, cardioprotective therapies, and secondary prevention approaches.

This Special Issue highlights recent breakthroughs in reperfusion therapy, including next-generation thrombolytics, advances in percutaneous coronary intervention (PCI), and hybrid revascularization techniques aimed at optimizing myocardial salvage and reducing infarct size. Additionally, it explores novel mechanical circulatory support (MCS) devices such as Impella, intra-aortic balloon pumps (IABPs), and extracorporeal membrane oxygenation (ECMO) for high-risk AMI patients and those with cardiogenic shock.

Emerging lipid-lowering and cardioprotective therapies such as bempedoic acid, PCSK9 inhibitors (PCSK9i), and GLP-1 receptor agonists (GLP-1 RA) are discussed for their potential to improve long-term cardiovascular outcomes. Advances in electrophysiological management, including bundle branch pacing (BBP) and cardiac contractility modulation (CCM), are explored as alternatives for improving post-AMI heart function.

This Special Issue also investigates the role of artificial intelligence and precision medicine in refining risk stratification and treatment personalization. Furthermore, regenerative and anti-inflammatory therapies offer promising prospects for myocardial repair and reducing post-AMI complications such as heart failure and arrhythmias.

By presenting these advancements, this Special Issue aims at equipping healthcare professionals with the latest knowledge and innovative strategies to enhance AMI management, improve survival rates, and reduce long-term complications.

Dr. Ciro Mauro
Dr. Fulvio Cacciapuoti
Guest Editors

Dr. Gerardo Carpinella
Guest Editor Assistant

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Keywords

  • acute myocardial infarction
  • early-phase management
  • long-term cardiac care
  • mechanical circulatory support
  • intensive care
  • lipid-lowering and cardioprotective therapies
  • precision medicine and artificial intelligence in cardiology

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Published Papers (1 paper)

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Research

26 pages, 2525 KB  
Article
Beyond Black Boxes: Interpretable AI with Explainable Neural Networks (ENNs) for Acute Myocardial Infarction (AMI) Using Common Hematological Parameters
by Zeynep Kucukakcali and Ipek Balikci Cicek
Medicina 2025, 61(9), 1552; https://doi.org/10.3390/medicina61091552 - 29 Aug 2025
Viewed by 518
Abstract
Background and Objectives: This study aims to evaluate the diagnostic potential of routinely available hematological parameters for acute myocardial infarction (AMI) by employing an Explainable Neural Network (ENN) model that combines high predictive accuracy with interpretability. Materials and Methods: A publicly [...] Read more.
Background and Objectives: This study aims to evaluate the diagnostic potential of routinely available hematological parameters for acute myocardial infarction (AMI) by employing an Explainable Neural Network (ENN) model that combines high predictive accuracy with interpretability. Materials and Methods: A publicly available dataset comprising 981 individuals (477 AMI patients and 504 controls) was analyzed. A broad set of hematological features—including white blood cell subtypes, red cell indices, and platelet-based markers—was used to train an ENN model. Bootstrap resampling was applied to enhance model generalizability. The model’s performance was assessed using standard classification metrics such as accuracy, sensitivity, specificity, F1-score, and Matthews Correlation Coefficient (MCC). SHapley Additive exPlanations (SHAP) were employed to provide both global and individualized insights into feature contributions. Results: The study analyzed hematological and biochemical parameters of 981 individuals. The explainable neural network (ENN) model demonstrated excellent diagnostic performance, achieving an accuracy of 94.1%, balanced accuracy of 94.2%, F1-score of 93.9%, and MCC of 0.883. The AUC was 0.96, confirming strong discriminative ability. SHAP-based explainability analyses highlighted neutrophils (NEU), white blood cells (WBC), RDW-CV, basophils (BA), and lymphocytes (LY) as the most influential predictors. Individual- and class-level SHAP evaluations revealed that inflammatory and erythrocyte-related parameters played decisive roles in AMI classification, while distributional analyses showed narrower parameter ranges in healthy individuals and greater heterogeneity among patients. Conclusions: The findings suggest that cost-effective, non-invasive blood parameters can be effectively utilized within interpretable AI frameworks to enhance AMI diagnosis. The integration of ENN with SHAP provides a dual benefit of diagnostic power and transparent rationale, facilitating clinician trust and real-world applicability. This scalable, explainable model offers a clinically viable decision-support tool aligned with the principles of precision medicine and ethical AI. Full article
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