A narrative review: predicting liver transplant graft survival using artificial intelligence modeling - Gulla, Aiste; Jakiunaite, Ieva; Juchneviciute, Ivona; Dzemyda, Gintautas (Frontiers in Transplantation, 2024)
Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict short-and long-term survival of the liver graft. Recently, artificial intelligence models are used to evaluate short-and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this studynarrative review, we reviewed 15 studies published in PubMed, Web of Science and Cohrane databases in years 2017-2022 concerning liver transplantations. We picked out 17 studies using our selection criteria and analyzed them ose studies, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In ten eight studies, models estimating only short-term liver graft survival were created, while in five eight of the studies models for prediction of only long-term liver graft survival were built. In four of the studies artificial intelligence algorithms evaluating both short-and long-term liver graft survival were created. The most important Mmedical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were recipient's age, recipient's BMI, creatinine levels in recipient's serum, recipient's INR, diabetes mellitus and recipient's MELD score. To conclude, in order to define important liver transplantation characteristics, that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.