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Liver Transplantation

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Results of Green Indocyanine in the Use of the R1T1 Robot as Aid in the Pre-operative Process of Hepatic Organ Transplant: Experiment in Wistar Rats.

Transplantation proceedings
Since the beginning of the history of transplants, numerous difficulties have been faced in the effective implementation of this therapeutic practice, especially with regard to the transplantation of solid organs and their teaching and training, toge...

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Hepatology (Baltimore, Md.)
Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas wi...

Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data.

Transplantation proceedings
Prediction models of post-liver transplant mortality are crucial so that donor organs are not allocated to recipients with unreasonably high probabilities of mortality. Machine learning algorithms, particularly deep neural networks (DNNs), can often ...

Identifying Factors That Affect Patient Survival After Orthotopic Liver Transplant Using Machine-Learning Techniques.

Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation
OBJECTIVES: Survival after liver transplant depends on pretransplant, peritransplant, and posttransplant factors. Identifying effective factors for patient survival after transplant can help transplant centers make better decisions.

Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

International journal of computer assisted radiology and surgery
PURPOSE: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being ...

Adipokines, inflammatory mediators, and insulin-resistance parameters may not be good markers of metabolic syndrome after liver transplant.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVE: The role of adipokines in liver transplantation (LTx) recipients who have metabolic syndrome (MetS) has seldom been assessed. The aim of this study was to investigate the concentrations of adipokines, inflammatory mediators, and insulin-re...

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...

Artificial intelligence, machine learning, and deep learning in liver transplantation.

Journal of hepatology
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and o...

Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

Current opinion in organ transplantation
PURPOSE OF REVIEW: Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possible liver transplant candidates, and assisting in the heterogeneous field of donor-recipient (D-R) ma...