AIMC Topic: Liver Transplantation

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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
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 ...

Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem.

Artificial intelligence in medicine
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...

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...

Personalized survival benefit estimation from living donor liver transplantation with a novel machine learning method for confounding adjustment.

Journal of hepatology
BACKGROUND & AIMS: Addressing many clinical questions, such as estimating survival differences between living donor (LDLT) and deceased donor liver transplantation (DDLT), relies on observational studies, as randomized-controlled trials (RCTs) are of...

Identification of key factors and explainability analysis for surgical decision-making in hepatic alveolar echinococcosis assisted by machine learning.

World journal of gastroenterology
BACKGROUND: Echinococcosis, caused by Echinococcus parasites, includes alveolar echinococcosis (AE), the most lethal form, primarily affecting the liver with a 90% mortality rate without prompt treatment. While radical surgery combined with antiparas...

Digital Pathology Quantification of the Continuum of Cirrhosis Severity in Human Liver Biopsies.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: Liver biopsy is the gold standard for assessing fibrosis in cirrhotic livers, yet cirrhosis is spatially heterogeneous and continuously remodels. This study evaluates a novel phenotypic digital pathology platform for continuous f...

Liver Fat Fraction and Machine Learning Improve Steatohepatitis Diagnosis in Liver Transplant Patients.

NMR in biomedicine
Machine learning identifies liver fat fraction (FF) measured by H MR spectroscopy, insulinemia, and elastography as robust, non-invasive biomarkers for diagnosing steatohepatitis in liver transplant patients, validated through decision tree analysis....