AIMC Topic: Graft Survival
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Effect of the vascularized bone components on the survival of vascularized composite allografts.
The Journal of surgical research
Apr 6, 2017
BACKGROUND: Vascularized composite allograft (VCA), such as hand and face allograft, contains a vascularized bone component that may provide an immunologic benefit and induce tolerance for the simultaneous inclusion of marrow cells and a marrow micro...
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...
Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.
Academic radiology
Aug 1, 2025
RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among ...
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)...
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