PCI

Machine learning-based prediction of postoperative restenosis risk following direct repair of paediatric coarctation of the aorta.

OBJECTIVES: Coarctation of the aorta is a congenital cardiovascular disease with focal aortic luminal narrowing, and paediatric patients face a high postoperative restenosis risk. This study aimed to develop and validate an interpretable machine learning model for early predicting restenosis after paediatric coarctation of the aorta direct repair using preoperative and intraoperative data. METHODS...

Thrombus imaging features influencing the choice of first-line thrombectomy strategy: an international survey of neurointerventionists.

INTRODUCTION: Endovascular thrombectomy (EVT) is the standard of care for large vessel occlusion stroke, but the optimal first-line strategy remains debated. Thrombus imaging features may help guide device selection, yet their impact on neurointerventionists' decision-making is unclear. METHODS: We conducted an international anonymous survey distributed through JENI and ESMINT networks (January-Ap...

Machine learning-derived clinical phenotypes of ascites in cirrhosis: A multi-center latent class analysis with external validation.

BACKGROUND: Ascites development in cirrhosis reduces five-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comp...

Deep Learning-Based Assessment of Coronary Artery Morphologies for Predicting Responsiveness to Intravascular Lithotripsy.

BACKGROUND: Although intravascular lithotripsy (IVL) has proven effective in facilitating luminal expansion through calcification modification, its ef...

Interpretable Machine Learning Models for Mortality Prediction in Critically Ill Patients with Intra-Aortic Balloon Pump Therapy: Development and Evaluation.

BACKGROUND: This study aims to develop and validate predictive models to assess mortality risk among patients undergoing intra-aortic balloon pump (IA...

Seeing Beyond the Surgeon's Eye: Artificial Intelligence for Postoperative Monitoring of Free Vascularized Flaps: A Narrative Review.

BACKGROUND: Postoperative monitoring of free vascularized flaps (FVFs) is critical for early detection of ischemia and timely salvage. Although tradit...

Machine Learning for Predicting Critical Postoperative Interventions: Proof-of-Concept Study Using the INSPIRE Dataset.

BACKGROUND: Postoperative complications contribute significantly to patient morbidity and mortality. Early prediction of such complications could enab...

Mechanics, Fracture, and Fragmentation of Thrombi: A Computational Study Review.

Thrombosis drives myocardial infarction, acute ischemic stroke and pulmonary embolism, yet more than half of patients undergoing mechanical thrombecto...

Risk Factors for Coronary Heart Disease and Stroke Among Older Adults With Metabolic Dysfunction-Associated Steatotic Liver Disease: A Nationwide Cohort Study.

BACKGROUND: Older adults with metabolic dysfunction-associated steatotic liver disease have elevated cardiovascular disease (CVD) risk, yet convention...

First-pass aspiration versus stent retriever thrombectomy: machine learning study of treatment effect heterogeneity.

BACKGROUND: Randomized trials have shown equivocal outcomes between a direct aspiration as first-pass technique (ADAPT) and stent retriever (SR) strok...

Preoperative artificial intelligence-based risk model for surgical reintervention after microsurgical free flap reconstruction.

BACKGROUND: Flap-related vascular complications requiring surgical reintervention remain a source of morbidity after microsurgical free flap reconstru...

A New Tool for a Common Problem: Super-Resolution Deep-Learning Reconstruction for Improving Coronary CTA Diagnosis of In-Stent Restenosis.

[Overview and conclusions of the STROKE data platform of the National Laboratory for Translational Neuroscience and the associated clinical studies].

Acute ischemic and hemorrhagic stroke are among the leading causes of mortality and long-term disability worldwide. In addition to the results of rand...

Precision Medicine for Anticoagulation Strategies in the Cath Lab: Part 2.

PURPOSE OF REVIEW: Intraprocedural anticoagulation during percutaneous coronary intervention (PCI) remains particularly challenging in high-risk and u...

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer scr...

Comparison of Super-Resolution Deep-Learning Reconstruction and Hybrid Iterative Reconstruction for Coronary Stent Assessment on CTA: A Prospective Multicenter Study.

Background: Conventional deep-learning reconstruction (DLR) CT methods provide noise reduction but limited improvements in spatial resolution. Objecti...

Non-invasive arterial blood pressure waveform generation in critically ill patients: A sensor-based deep learning approach.

Continuous monitoring of Arterial Blood Pressure (ABP) in critically ill patients requires invasive arterial catheterization, which carries risks of t...

Transformer-DAPT: AI-based dynamic assessment of ischemic and bleeding risks in patients on DAPT following PCI.

Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) is traditionally guided by rule-based scores providing static, sin...

Procedural Strategies for Optimal Transcatheter Aortic Valve Replacement: An International Position Statement.

The extension of transcatheter aortic valve replacement (TAVR) to younger patients with longer life expectancy has driven a shift in focus toward proc...

Artificial intelligence with mucosal exposure devices versus artificial intelligence alone in detection of colorectal adenomas: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Both artificial intelligence (AI) and mucosal exposure devices (MEDs) have been shown to improve adenoma detection rate and reduc...