AIMC Topic: Percutaneous Coronary Intervention

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Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Prospective Cohort Study.

Journal of medical Internet research
BACKGROUND: Given the challenges faced during percutaneous coronary intervention (PCI) for heavily calcified lesions, accurately predicting PCI success is crucial for enhancing patient outcomes and optimizing procedural strategies.

Current and future applications of robotics in structural heart interventions.

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology
Robotics entered the cardiovascular field in the late 1990s with a robot-assisted coronary artery bypass graft. Since then, the use of robots has become a common part of cardiovascular surgery in several types of interventions. The experience in tran...

Comparison of machine learning models with conventional statistical methods for prediction of percutaneous coronary intervention outcomes: a systematic review and meta-analysis.

BMC cardiovascular disorders
INTRODUCTION: Percutaneous coronary intervention (PCI) has been the main treatment of coronary artery disease (CAD). In this review, we aimed to compare the performance of machine learning (ML) vs. logistic regression (LR) models in predicting differ...

Chronic Total Occlusion Percutaneous Coronary Intervention: Present and Future.

Circulation. Cardiovascular interventions
Chronic total occlusion percutaneous coronary intervention has evolved into a subspecialty of interventional cardiology. Using a variety of antegrade and retrograde techniques, experienced operators currently achieve success rates of 85% to 90%, with...

Machine Learning-Based Algorithm to Predict Procedural Success in a Large European Cohort of Hybrid Chronic Total Occlusion Percutaneous Coronary Interventions.

The American journal of cardiology
CTOs are frequently encountered in patients undergoing invasive coronary angiography. Even though technical progress in CTO-PCI and enhanced skills of dedicated operators have led to substantial procedural improvement, the success of the intervention...

An interpretable radiomics-based machine learning model for predicting reverse left ventricular remodeling in STEMI patients using late gadolinium enhancement of myocardial scar.

European radiology
OBJECTIVES: To evaluate the added value of the late gadolinium enhancement (LGE)-scar radiomics features in predicting reverse left ventricular remodeling (r-LVR) in ST-segment elevation myocardial infarction (STEMI) patients using machine learning (...

Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.

Heart, lung & circulation
BACKGROUND: Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and, most importantly, for clinical decision-making. This systematic review aims to ...

A brief history and clinical use of robotic procedures in the cardiovascular system.

Kardiologia polska
Robotic-assisted percutaneous coronary intervention (r-PCI) exemplifies the advancement of interventional cardiology by integrating robotics to improve procedural control, operator safety, and potentially patient outcomes. This is a brief history of ...

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Computers in biology and medicine
Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient management and quality improvement in healthcare. However, achieving accurate predictions requires the integration of multimodal clinical data, includi...