摘要
Since its implementation almost two decades ago,the urgency allocation policy has improved the survival of patients on the waiting list for liver transplantation worldwide.The Model for End-Stage Liver Disease score is widely used to predict waiting list mortality.Due to some limitations related to its use,there is an active investigation to develop other prognostic scores.Liver allocation(LA)entails complex decision-making,and grafts are occasionally not directed to the recipients who are more likely to survive.Prognostic scores have,thus far,failed to predict post-operatory survival.Furthermore,the increasing use of marginal donors is associated with worse outcomes.Adequate donor-recipient pairing could help avoid retransplantation or futile procedures and reduce postoperative complications,mortality,hospitalization time,and costs.Artificial intelligence has applications in several medical fields.Machine learning algorithms(MLAs)use large amounts of data to detect unforeseen patterns and complex interactions between variables.Artificial neural networks and decision trees were the most common forms of MLA tested on LA.Some researchers have shown them to be superior for predicting waiting list mortality and graft failure than conventional statistical methods.These promising techniques are increasingly being considered for implementation.
