PCI

Deep learning predicts stent implantation in borderline coronary lesions from angiography.

Accurate evaluation of coronary intermediate lesions (50-70% stenosis) is essential for stent decision-making, yet conventional angiography remains subjective and adjunctive tests like FFR are often invasive or costly. In this retrospective multicenter study of 1298 patients, we developed an attention-enhanced deep learning model using Improved_EfficientNet with a Convolutional Block Attention Mod...

Gene expression integration and similarity score based modeling improve risk stratification in idiopathic venous thrombophilia.

BACKGROUND: Idiopathic venous thromboembolism (VTE) occurs in the absence of provoking factors, limiting the efficacy of current risk stratification. In parallel, the lack of integration between transcriptomic data and established risk factors prevents the identification of individuals with a high baseline predisposition. OBJECTIVES: To improve risk stratification of idiopathic VTE beyond traditio...

Hidden Preoperative Lower-Extremity Venous Thrombosis in Pelvic Organ Prolapse Surgery: Prevalence and Development of a Clinical Prediction Model.

INTRODUCTION AND HYPOTHESIS: Preoperative lower-extremity venous thrombosis (LEVT) is frequently overlooked in women undergoing pelvic organ prolapse ...

Interpretable machine learning using accumulated local effects to characterise predictors of subclinical leaflet thrombosis after self-expanding transcatheter aortic valve implantation.

OBJECTIVES: Subclinical leaflet thrombosis is an early form of bioprosthetic valve dysfunction after transcatheter aortic valve implantation. Predicti...

Accuracy of Diagnosis in Myeloproliferative Neoplasms With Splanchnic Vein Thrombosis (MPN-SVT).

Machine Learning Prediction for 6-Month Outcomes in Traumatic Cerebral Venous Sinus Thrombosis.

This study developed and externally validated a multicenter machine learning framework to predict 6-month poor functional outcome (modified Rankin Sca...

Computational Model for Predicting Optimal Clinical Intervention in Pre-Operative Neonates with Transposition of the Great Arteries.

PURPOSE: Transposition of the Great Arteries (TGA) is a congenital heart defect characterized by the abnormal positioning of the pulmonary artery over...

Machine learning-based model for predicting recanalization in isolated distal deep vein thrombosis and analysis of predictors.

BACKGROUND: Isolated distal deep vein thrombosis (IDDVT) is common, yet tools for predicting poor recanalization remain limited. We aimed to develop a...

Thrombosis at the crossroads of inflammation, coagulation, and vascular biology: Recent advances and emerging therapeutic strategies.

Thrombosis is a multifaceted pathological process involving intravascular clot formation that drives a range of cardiovascular and cerebrovascular dis...

MORGAS (MORtality ORiented Glucose, Age, Smoking, and Stent Score) predicts mortality after percutaneous coronary intervention.

BACKGROUND: Accurate prediction of mortality after percutaneous coronary intervention (PCI) remains a clinical challenge. Existing risk scores often l...

Rationale and design of a multicenter, prospective, diagnostic clinical study: A study protocol for evaluating the diagnostic validation of deep learning-based noninvasive CT-FFR for in-stent restenosis.

INTRODUCTION: Currently, there are many diagnostic strategies for in-stent restenosis (ISR) used clinically, including invasive coronary angiography (...

Anatomical deformation prediction using artificial intelligence for fusion imaging during aortic endovascular procedures.

Placement of an aortic stent graft induces anatomical deformations, particularly at the iliac level, because of the introduction of rigid devices. Ant...

Computational and machine learning approach for nanoparticle-enhanced bio-heat transport with coupled effects of interparticle spacing and nanoparticles radius.

Targeted delivery of drugs and hyperthermia in cardiovascular disease demand the accurate delivery of nanoparticles in complex arterial geometries. Th...

[Machine learning models to predict intestinal necrosis in acute mesenteric venous thrombosis].

Objective: To develop and validate machine learning-based models for predicting the risk of transmural irreversible intestinal necrosis (ITIN) in pati...

Decoding Kounis syndrome with artificial intelligence: from diagnosis to precision medicine.

Kounis syndrome-acute coronary events triggered by allergic or hypersensitivity reactions-remains underrecognized across emergency and cardiology sett...

Editorial: future of ureteral stents: what do we need? a pearls opinion.

Ureteral stents, introduced in 1960s, are essential for managing upper urinary tract pathologies, maintaining ureteral patency in conditions like obst...

AI in Point-of-Care Imaging for Clinical Decision Support: Systematic Review of Diagnostic Accuracy, Task-Shifting, and Explainability.

BACKGROUND: Artificial intelligence (AI) integrated with point-of-care imaging is a promising approach to expand access in settings with limited speci...

Anatomically parameterised statistical shape modelling of LVAD-supported left ventricles for thrombosis risk assessment.

Heart failure remains a major global burden, and limited donor availability has made left ventricular assist devices (LVADs) a critical alternative. H...

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical and angiographic characteristics. We hypothesized ...

An AI Based Screening Tool for Detection of Cerebral Venous Thrombosis on Non-Contrast Brain CT.

While non-contrast computed tomography (NCCT) is the primary ‎imaging modality in emergency settings, it has a low sensitivity for detection of ‎Cereb...