Company or Institution

Vilnius University Institute of Digital Technologies and Data Science/ Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, Lithuania

Industry

Health

Website

https://vu.lt

Country

Lithuania

Sustainable Development Goals (SDGs)

General description of the AI solution

Like in other fields of medicine, artificial intelligence and its integral part, machine learning, are gaining momentum in the field of liver transplantation. This will undoubtedly improve the accuracy of the prognosis of transplantation processes success, allow the selection of the most influential factors among many that influence morbidity and mortality after transplantation, and optimize the process of organ allocation to patients awaiting transplantation. In addition, such analysis can be used to predict disease recurrence and manage immunosuppression. The most commonly used machine learning models are: decision trees, random forest, Bayesian, k-nearest neighbors, support vector machine classifiers and ensembles of classifiers. Since organ transplantation is concerned with the ethics and fairness of organ allocation to patients, the limitations associated with it should also be considered. During the project, a set of numerical data on liver transplantology will be created, reflecting the situation in Lithuania. Based on it, the machine learning model(s) will be selected, which will best allow making important preoperative, surgical and postoperative decisions and obtain reliable predictions in liver transplant patients.

Publications

1.Ferrarese A. Sartori G. et al. Machine learning in liver transplantation: a tool for some unsolved questions? Transplant International 2021; 34:398-411.

2.Speiser JL, Karvellas CJ, Wolf BJ, Chung D, Koch DG, Durkalski VL. Predicting daily outcomes in acetaminophen-induced acute liver failure patients with machine learning techniques. Comput Methods Programs Biomed 2019; 175:111.

3. Khosravi B, Pourahmad S, Bahreini A, Nikeghbalian S, Mehrdad G. Five years survival of patients after liver transplantation and its effective factors by neural network and cox proportional hazard regression models, Hepat Mon 2015; 15: e25164.

4.Marsh JW, Dvorchik I, Subotin M, et al. The prediction of risk of recurrence and time to recurrence of hepatocellular carcinoma after orthotopic liver transplantation: a pilot study. Hepatology 1997; 26:444.

5.Lee HC, Yoon SB, Yang SM et al. Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression mode. J Clin Med 2018; 7:428.

Needs