1. Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons
- Author
-
Beatriz Pontes Balanza, Juan M. Castillo Tuñón, Daniel Mateos García, Javier Padillo Ruiz, José C. Riquelme Santos, José M. Álamo Martinez, Carmen Bernal Bellido, Gonzalo Suarez Artacho, Carmen Cepeda Franco, Miguel A. Gómez Bravo, and Luis M. Marín Gómez
- Subjects
liver transplants ,machine learning ,decision-making process ,liver graft assessment ,artificial intelligence ,Surgery ,RD1-811 - Abstract
BackgroundThe complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.Material and methodLiver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated “in situ” for transplantation, and those discarded after the “in situ” evaluation were considered as no transplantable liver grafts, while those grafts transplanted after “in situ” evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.ResultsA total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p
- Published
- 2023
- Full Text
- View/download PDF