OBJECTIVE: Participation in the Vascular Quality Initiative (VQI) provides important resources to surgeons, but the ability to do so is often limited by time and data entry personnel. Large language models (LLMs) such as ChatGPT (OpenAI) are examples...
Advances in experimental medicine and biology
39523278
The advent of different realms of computational neurosurgery-including not only machine intelligence but also visualization techniques such as mixed reality and robotic applications-is beginning to impact both open vascular as well as endovascular ne...
Journal of the American Heart Association
39189482
BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke o...
Annals of clinical and translational neurology
39180278
OBJECTIVE: Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to d...
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
39237055
OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compliance and relatively large variability in measurement methods of abdominal aortic aneurysm (AAA) sac size after treatment. Measuring volume offers a m...
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-r...
European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
39638233
OBJECTIVE: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of ...
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.
OBJECTIVE: This study was to explore the factors associated with prolonged hospital length of stay (LOS) in patients with intracranial aneurysms (IAs) undergoing endovascular interventional embolization and construct prediction model machine learning...