AIMC Topic: Liver Transplantation
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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
Jul 1, 2025
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
Jul 1, 2025
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....
Comparison of Sarcopenia Assessment in Liver Transplant Recipients by Computed Tomography Freehand Region-of-Interest versus an Automated Deep Learning System.
Clinical transplantation
Jun 1, 2025
INTRODUCTION: Sarcopenia, or the loss of muscle quality and quantity, has been associated with poor clinical outcomes in liver transplantation such as infection, increased length of stay, and increased patient mortality. Abdominal computed tomography...
GraftIQ: Hybrid multi-class neural network integrating clinical insight for multi-outcome prediction in liver transplant recipients.
Nature communications
May 28, 2025
Liver transplant recipients (LTRs) are at risk of graft injury, leading to cirrhosis and reduced survival. Liver biopsy, the diagnostic gold standard, is invasive and risky. We developed a hybrid multi-class neural network (NN) model, 'GraftIQ,' inte...
Identifying shared hub genes in LIRI and MASLD through bioinformatics analysis and machine learning.
Scientific reports
May 27, 2025
Patients with metabolic dysfunction-associated steatotic liver disease (MASLD) are more susceptible to liver ischemia-reperfusion injury (LIRI), complicating liver surgery outcomes. This study aimed to uncover shared hub genes and mechanisms linking ...
Patient Survival Prediction by Analyzing Pathological Images of Patients After Liver Transplantation.
Studies in health technology and informatics
May 15, 2025
Predicting whether a patient will develop cancer using nuclear features on pathological images is important for decision making regarding patient treatment after liver transplantation or hepatectomy. Unlike manual segmentation to extract nuclei parts...
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...
Post-Transplant Liver Monitoring Utilizing Integrated Surface-Enhanced Raman and AI in Hepatic Ischemia-Reperfusion Injury Animal Model.
International journal of nanomedicine
Jan 1, 2025
BACKGROUND: While liver transplantation saves lives from irreversible liver damage, it poses challenges such as graft dysfunction due to factors like ischemia-reperfusion (IR) injury, which can lead to significant cellular damage and systemic complic...
Artificial intelligence, machine learning, and deep learning in liver transplantation.
Journal of hepatology
Jun 1, 2023
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
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...
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