AIMC Topic: Liver Transplantation

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Technological Innovation in Outpatient Assistance for Chronic Liver Disease and Liver Transplant Patients During the Coronavirus Disease Outbreak: A Method to Minimize Transmission.

Clinics (Sao Paulo, Brazil) May 29, 2020
Ambulatory Care Outpatients End Stage Liver Disease Betacoronavirus Liver Transplantation Coronavirus Infections Transplant Recipients Telemedicine Pneumonia, Viral Humans Pandemics COVID-19 SARS-CoV-2 Robotics
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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 May 24, 2020
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...
Liver Transplantation Humans Indocyanine Green Animals Liver Function Tests Rats Transplants Liver Robotics Male Rats, Wistar
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Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review.

Hepatology (Baltimore, Md.) Mar 6, 2020
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...
Patient Selection Liver Diseases Artificial Intelligence Machine Learning Algorithms Humans Liver Transplantation Neural Networks, Computer
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Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data.

Transplantation proceedings Jan 8, 2020
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 ...
Adult Male Deep Learning ROC Curve Living Donors Humans Computer Simulation Female Registries Liver Transplantation
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Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data.

Pediatric transplantation Jul 22, 2019
Machine learning analyses allow for the consideration of numerous variables in order to accommodate complex relationships that would not otherwise be apparent in traditional statistical methods to better classify patient risk. The SPLIT registry data...
Pediatrics Anastomosis, Surgical Liver Failure Predictive Value of Tests Biliary Tract Surgical Procedures Registries Liver Transplantation Adolescent Child, Preschool Child Treatment Outcome Machine Learning Humans Software Risk Factors Adult Algorithms Young Adult Prospective Studies Risk Assessment Infant
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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 Apr 9, 2019
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.
Aged Young Adult Adult Cross-Sectional Studies Male Middle Aged Risk Assessment Adolescent Decision Support Techniques Retrospective Studies Female Risk Factors Humans Machine Learning Liver Transplantation Iran Data Mining Time Factors Treatment Outcome Health Status Predictive Value of Tests
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Computer-assisted liver graft steatosis assessment via learning-based texture analysis.

International journal of computer assisted radiology and surgery May 23, 2018
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 ...
Image Interpretation, Computer-Assisted Pattern Recognition, Automated Algorithms Fatty Liver Humans Liver Machine Learning Color Liver Transplantation
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Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem.

Artificial intelligence in medicine Feb 20, 2017
OBJECTIVE: Create an efficient decision-support model to assist medical experts in the process of organ allocation in liver transplantation. The mathematical model proposed here uses different sources of information to predict the probability of orga...
Neural Networks, Computer Algorithms Decision Support Techniques Models, Theoretical Humans Liver Transplantation Liver Failure
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Adipokines, inflammatory mediators, and insulin-resistance parameters may not be good markers of metabolic syndrome after liver transplant.

Nutrition (Burbank, Los Angeles County, Calif.) Dec 31, 2015
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...
Fatty Acids, Nonesterified Postoperative Complications Female Adipokines Humans Chemokine CCL2 Insulin Resistance Resistin Cross-Sectional Studies Tumor Necrosis Factor-alpha Liver Transplantation Metabolic Syndrome Male Interleukin-6 Inflammation Mediators C-Reactive Protein Middle Aged Biomarkers Adiponectin
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Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction.

European journal of medical research Aug 20, 2025
BACKGROUND: To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimise clinical decision-making in liver transplantation (LT) recipients.
Humans Prognosis Retrospective Studies Female Risk Assessment Postoperative Complications Nomograms Liver Transplantation End Stage Liver Disease Risk Factors Adult Male Middle Aged
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