Machine learning in liver transplantation: a tool for some unsolved questions?. (4th February 2021)

• Record Type: Journal Article
• Authors: Ferrarese, Alberto; Sartori, Giuseppe; Orrù, Graziella; Frigo, Anna Chiara; Pelizzaro, Filippo; Burra, Patrizia; Senzolo, Marco
• Is Part Of: Transplant international. Volume 34:Number 3(2021)
• Page Start: 398
• Page End: 411
• Publication Date: 2021-02-04
• Subjects: acute liver failure -- cirrhosis -- liver transplantation -- machine learning -- neural network; Transplantation of organs, tissues, etc -- Periodicals; 617.95405
• DOI: 10.1111/tri.13818
• Languages: English
• ISSNs: 0934-0874

Abstract

Summary: Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor‐to‐recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.