Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

Introduction: A simple risk stratification model to predict aneurysm sac shrinkagein patients undergoing endovascular aortic repair (EVAR) for abdominal aortic aneurysms (AAA) was developed using machine learning-based decision tree analysis.

Methods: One hundred nineteen patients with AAA who underwent elective EVAR at Tokyo Medical University Hospital between November 2013 and July 2019 were included in the study. Predictors of aneurysm sac shrinkage identified in univariable analysis (P < 0.

Predicting Short-Term Mortality after Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

Background: Endovascular aneurysm repair (EVAR) has revolutionized the treatment of abdominal aortic aneurysms by offering a less invasive alternative to open surgery. Understanding the factors that influence patient outcomes, particularly for high-risk patients, is crucial. The aim of this study was to determine whether machine learning (ML)-based decision tree analysis (DTA), a subset of artificial intelligence, could predict patient outcomes by identifying complex patterns in data.

Feasibility and Safety of the Distal Transradial Access for Noncardiac Intervention.

Purpose: Distal transradial access through the anatomical snuffbox has been highlighted in recent research because it provides extremely low invasiveness. It has demonstrated its feasibility and safety for cardiac intervention. However, its characteristics for noncardiac intervention are not well known.