AIMC Topic: Graft Survival
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Histone deacetylase 2 is decreased in peripheral blood pro-inflammatory CD8+ T and NKT-like lymphocytes following lung transplant.
Respirology (Carlton, Vic.)
Nov 6, 2016
BACKGROUND AND OBJECTIVE: Immunosuppression therapy following lung transplantation fails to prevent chronic rejection in many patients, which is associated with lack of suppression of cytotoxic mediators and pro-inflammatory cytokines in peripheral b...
Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT Features.
Clinical transplantation
Jul 1, 2025
INTRODUCTION: Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.
Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.
Current opinion in organ transplantation
Jun 1, 2025
PURPOSE OF REVIEW: The objective of this review is to provide an update on the application of artificial intelligence (AI) for the histological interpretation of kidney transplant biopsies.
Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.
Clinical transplantation
Apr 1, 2025
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...
Live-Donor Kidney Transplant Outcome Prediction (L-TOP) using artificial intelligence.
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
Nov 27, 2024
BACKGROUND: Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the currently used models are of limited discriminative or calibration power and there is a critical need to im...
Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.
Current opinion in organ transplantation
Aug 1, 2020
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...
Artificial neural network and bioavailability of the immunosuppression drug.
Current opinion in organ transplantation
Aug 1, 2020
PURPOSE OF REVIEW: The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees ...
Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.
Studies in health technology and informatics
Aug 21, 2019
Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML)...
Re: Minimally Invasive, Robot-Assisted Procedure for Kidney Transplantation among Morbidly Obese: Positive Outcomes at 5 Years Post-Transplant.
The Journal of urology
Jun 1, 2019
Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.
Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
Feb 1, 2018
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to ...
Liver Diseases
Donor Selection
London
Area Under Curve
Liver Transplantation
Waiting Lists
Female
Humans
Reproducibility of Results
Risk Factors
Adult
ROC Curve
Male
Neural Networks, Computer
Algorithms
Middle Aged
Tissue Donors
Decision Support Techniques
Risk Assessment
Graft Survival
Computer Simulation
Time Factors
Treatment Outcome
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