Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW estimation. Data from 872 consecutive living donors of a left lateral sector, left lobe, or right lobe to adults or children for living-related liver transplantation were collected from January 2011 to December 2019.

Large for size in pediatrics liver transplant using left lateral segment grafts: A single center experience.

Background: There are still controversies in using the large left lateral segment in pediatrics LT, with the possibility of the problem of LFS grafts, and the use of monosegmental or reduced liver grafts in small infants. This study aimed to evaluate our experience with LFSG in pediatrics LT.

Methods: A cohort retrospective analysis was conducted including pediatric recipients who underwent LT between January 2011 and October 2019.