Jason Tran1, Divya Sharma2,3, Neta Gotlieb4, Wei Xu2,3, Mamatha Bhat5,6. 1. Department of Medicine, University of Ottawa, Ottawa, Canada. 2. Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada. 3. Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada. 4. Ajmera Transplant Program, University Health Network, Toronto, ON, Canada. 5. Ajmera Transplant Program, University Health Network, Toronto, ON, Canada. mamatha.bhat@uhn.ca. 6. Division of Gastroenterology, Department of Medicine, University of Toronto, 585 University Avenue, Toronto, ON, M5G 2N2, Canada. mamatha.bhat@uhn.ca.
Abstract
BACKGROUND: Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM: In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS: Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION: ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
BACKGROUND: Machine learning (ML) has been increasingly applied in the health-care and liver transplant setting. The demand for liver transplantation continues to expand on an international scale, and with advanced aging and complex comorbidities, many challenges throughout the transplantation decision-making process must be better addressed. There exist massive datasets with hidden, non-linear relationships between demographic, clinical, laboratory, genetic, and imaging parameters that conventional methods fail to capitalize on when reviewing their predictive potential. Pre-transplant challenges include addressing efficacies of liver segmentation, hepatic steatosis assessment, and graft allocation. Post-transplant applications include predicting patient survival, graft rejection and failure, and post-operative morbidity risk. AIM: In this review, we describe a comprehensive summary of ML applications in liver transplantation including the clinical context and how to overcome challenges for clinical implementation. METHODS: Twenty-nine articles were identified from Ovid MEDLINE, MEDLINE Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials. CONCLUSION: ML is vastly interrogated in liver transplantation with promising applications in pre- and post-transplant settings. Although challenges exist including site-specific training requirements, the demand for more multi-center studies, and optimization hurdles for clinical interpretability, the powerful potential of ML merits further exploration to enhance patient care.
Authors: Jasjit S Suri; Mrinalini Bhagawati; Sudip Paul; Athanasios D Protogerou; Petros P Sfikakis; George D Kitas; Narendra N Khanna; Zoltan Ruzsa; Aditya M Sharma; Sanjay Saxena; Gavino Faa; John R Laird; Amer M Johri; Manudeep K Kalra; Kosmas I Paraskevas; Luca Saba Journal: Diagnostics (Basel) Date: 2022-03-16