Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching?

Abstract

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered “unbalanced.” In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D–R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.

Smart match: revolutionizing organ allocation through artificial intelligence

“…The disparity between transplant candidates and available grafts is exacerbated by varying organ allocation policies, which range from prioritizing urgency (“sickest-first”) to favoring candidates with better clinical conditions based on “individual transplant benefit” and “population-based transplant benefit” principles. Consideration of risk factors, such as high-risk donors and recipients, may lead to avoidance in clinical practice, impacting high-risk, waitlisted transplant candidates negatively (Calleja Lozano et al, 2022 ). Traditional scoring systems, like logistic regression, have limitations in organ transplantation, assuming linear relationships, overlooking key variables, and struggling with unbalanced problems.…”

Artificial Intelligence in Organ Transplantation: Surveying Current Applications, Addressing Challenges and Exploring Frontiers

“…In addition, the utilization of AI in assessing grafts and matching donors with recipients has the potential to bring about significant advancements in the allocation system. The ultimate objective is to expedite transplantation procedures and improve overall outcomes [5,65]. Traditional models like SOFT or BAR scores often face challenges when accurately representing the complexities associated with donorrecipient matching [5].…”

“…The advanced predictive algorithms of ML show great potential, suggesting a significant and positive effect on the process of organ allocation in the coming years. These algorithms are expected to greatly improve the accuracy of determining whether a recipient is matched with specific organ donors, surpassing current traditional allocation methods [8,65]. By utilizing a wide range of factors and examining their intricate relationships, this developing approach has the potential to enhance the entire process of allocating organs.…”

Donor-recipient matching in adult liver transplantation: Current status and advances

“…AI is a branch of computational science that studies computational models capable of performing activities similar to human ones based on two characteristics: behavior and reasoning. Machine learning is defined as a branch of AI that focuses on using data and algorithms to mimic how humans learn, and gradually improve the accuracy of the algorithms (43).…”

“…However, no current allocation system is capable of achieving an ideal match. This means that these systems are unable to identify the candidate on the waiting list with the highest probability of death, and identify, among all available grafts, the one with the highest probability of post-transplant success for this candidate (43).…”