AI Medical Compendium Journal:
Clinical transplantation

Showing 1 to 10 of 10 articles

The impact of a dedicated operating room team on robotic transplant program growth and fellowship training.

Clinical transplantation
INTRODUCTION: Despite considerable interest in robotic surgery, successful incorporation of robotics into transplant programs has been challenging. Lack of a dedicated OR team with expertise in both robotics and transplant is felt to be a major barri...

The utility of machine learning for predicting donor discard in abdominal transplantation.

Clinical transplantation
BACKGROUND: Increasing access and better allocation of organs in the field of transplantation is a critical problem in clinical care. Limitations exist in accurately predicting allograft discard. Potential exists for machine learning to provide a bal...

State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database.

Clinical transplantation
PURPOSE: We sought to develop and validate machine learning (ML) models to increase the predictive accuracy of mortality after heart transplantation (HT).

Robot-assisted laparoscopic vs laparoscopic donor nephrectomy in renal transplantation: A meta-analysis.

Clinical transplantation
BACKGROUND: Currently, the robot-assisted laparoscopic donor nephrectomy (RDN) technique is used for live donor nephrectomy. Does it provide sufficient safety and benefits for living donors? We conducted a meta-analysis to assess the safety and effic...

Minimally invasive, robot-assisted procedure for kidney transplantation among morbidly obese: Positive outcomes at 5 years post-transplant.

Clinical transplantation
The pre-transplant weight loss required of end-stage renal disease patients is often unachievable. Though robot-assisted procedures among extremely obese have shown minimal complication, long-term outcomes are understudied. Previously, we reported no...

Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.

Clinical transplantation
INTRODUCTION: Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography...

Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

Clinical transplantation
BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent...

Using ChatGPT for Kidney Transplantation: Perceived Information Quality by Race and Education Levels.

Clinical transplantation
BACKGROUND: Kidney transplantation is a complex process requiring extensive preparation and ongoing monitoring. Artificial intelligence (AI)-powered chatbots hold potential for providing accessible health information, but our understanding of their r...

Transplant nephropathology: Wherefrom, wherein, and whereto.

Clinical transplantation
Renal pathology is a relatively recent entry in nephrology. While diseases of the kidney are old, their study began in the 19th century with the report of Richard Bright of the lesions of end-stage kidney disease. Its easy diagnosis from albuminuria ...