AIMC Topic: End Stage Liver Disease

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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
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.

The unwell patient with advanced chronic liver disease: when to use each score?

BMC medicine
BACKGROUND: Prognostication in chronic liver disease and the implementation of appropriate scoring systems is difficult given the variety of clinical manifestations. It is important to understand the limitations of each scoring system as well as the ...

Thigh muscle index as a valuable prognostic marker in middle-aged male patients undergoing liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
In liver transplantation (LT), determining the optimal recipients is crucial, and the MELD score has been used for this purpose. However, the MELD score does not reflect functional status, leading to the evaluation of sarcopenia. While the L3 skeleta...

Gender-Equity Model for Liver Allocation Using Artificial Intelligence (GEMA-AI) for Waiting List Liver Transplant Prioritization.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
BACKGROUND & AIMS: We aimed to develop and validate an artificial intelligence score (gender-equity model for liver allocation using artificial intelligence [GEMA-AI]) to predict liver transplantation (LT) waiting list outcomes using the same input v...

Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement.

Cardiovascular and interventional radiology
PURPOSE: To determine the association of machine learning-derived CT body composition and 90-day mortality after transjugular intrahepatic portosystemic shunt (TIPS) and to assess its predictive performance as a complement to Model for End-Stage Live...

Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

Computer methods and programs in biomedicine
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...

Deep learning and the future of the Model for End-Stage Liver Disease-sodium score.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.

Hepatology communications
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelli...

Artificial intelligence, machine learning, and deep learning in liver transplantation.

Journal of hepatology
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...