2022 | Early stage | Health | Lithuania | SDG3
Using machine learning to determine preoperative feasibility and postoperative outcomes of liver transplantation

Company or Institution

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







Sustainable Development Goals (SDGs)

SDG 3: Good Health and Well-being

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.


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International Research Centre
on Artificial Intelligence (IRCAI)
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