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Machine Learning Improves the Accuracy of Graft Weight Prediction in Living Donor Liver Transplantation by Mariano Cesare Giglio; Mario Zanfardino; Monica Franzese; Hazem Zakaria; Salah Alobthani; Ahmed Zidan; Islam Ismail Ayoub; Hany Abdelmeguid Shoreem; Boram Lee; Ho‐Seong Han; Andrea Della Penna; Silvio Nadalin; Roberto Ivan Troisi; Dieter Clemens Broering is a Medicine article available to read on EtoBox.
What is Machine Learning Improves the Accuracy of Graft Weight Prediction in Living Donor Liver Transplantation about?
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. Supervised machine‐learning models were trained (80% of observations) to predict GW using the following information: donor's age, sex, height, weight, and body mass index; graft type (left, right, or left lateral lobe); computed tomography estimated graft volume and total liver volume. Model performance was measured in a random independent set (20% of observations) and in an external validation cohort using the mean absolute error (MAE) and the mean absolute percentage error and compared with methods currently available for GW estimation. The best‐performing machine‐learning model showed an MAE value of 50 ± 62 g in predicting GW, with a mean error of 10.3%. These errors were significantly lower than those observed with altern
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- Author
- Mariano Cesare Giglio; Mario Zanfardino; Monica Franzese; Hazem Zakaria; Salah Alobthani; Ahmed Zidan; Islam Ismail Ayoub; Hany Abdelmeguid Shoreem; Boram Lee; Ho‐Seong Han; Andrea Della Penna; Silvio Nadalin; Roberto Ivan Troisi; Dieter Clemens Broering
- Publisher
- Ovid Technologies (Wolters Kluwer Health)
- Published
- 2023
- Language
- EN
- Field
- Medicine (Health Sciences)