Using Artificial Intelligence for Predicting Survival of Individual Grafts in Liver Transplantation: A Systematic Review.
| Title: | Using Artificial Intelligence for Predicting Survival of Individual Grafts in Liver Transplantation: A Systematic Review. |
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| Authors: | Wingfield LR; Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.; Ceresa C; Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.; Thorogood S; The School of Informatics, Informatics Forum, University of Edinburgh, Edinburgh, United Kingdom.; Fleuriot J; The School of Informatics, Informatics Forum, University of Edinburgh, Edinburgh, United Kingdom.; Knight S; Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom. |
| Source: | Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society [Liver Transpl] 2020 Jul; Vol. 26 (7), pp. 922-934. Date of Electronic Publication: 2020 Jun 23. |
| Publication Type: | Journal Article; Systematic Review |
| Language: | English |
| Journal Info: | Publisher: Wolters Kluwer Health, Inc Country of Publication: United States NLM ID: 100909185 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1527-6473 (Electronic) Linking ISSN: 15276465 NLM ISO Abbreviation: Liver Transpl Subsets: MEDLINE |
| Imprint Name(s): | Publication: 2023- : [Philadelphia] : Wolters Kluwer Health, Inc.; Original Publication: Philadelphia, PA : W.B. Saunders Co., c2000- |
| MeSH Terms: | End Stage Liver Disease*/surgery ; Liver Transplantation*/adverse effects; Artificial Intelligence ; Graft Survival ; Humans ; Living Donors ; Retrospective Studies ; Severity of Illness Index |
| Abstract: | The demand for liver transplantation far outstrips the supply of deceased donor organs, and so, listing and allocation decisions aim to maximize utility. Most existing methods for predicting transplant outcomes use basic methods, such as regression modeling, but newer artificial intelligence (AI) techniques have the potential to improve predictive accuracy. The aim was to perform a systematic review of studies predicting graft outcomes following deceased donor liver transplantation using AI techniques and to compare these findings to linear regression and standard predictive modeling: donor risk index (DRI), Model for End-Stage Liver Disease (MELD), and Survival Outcome Following Liver Transplantation (SOFT). After reviewing available article databases, a total of 52 articles were reviewed for inclusion. Of these articles, 9 met the inclusion criteria, which reported outcomes from 18,771 liver transplants. Artificial neural networks (ANNs) were the most commonly used methodology, being reported in 7 studies. Only 2 studies directly compared machine learning (ML) techniques to liver scoring modalities (i.e., DRI, SOFT, and balance of risk [BAR]). Both studies showed better prediction of individual organ survival with the optimal ANN model, reporting an area under the receiver operating characteristic curve (AUROC) 0.82 compared with BAR (0.62) and SOFT (0.57), and the other ANN model gave an AUC ROC of 0.84 compared with a DRI (0.68) and SOFT (0.64). AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. When compared with the standard techniques, AI methods are dynamic and are able to be trained and validated within every population. However, the high accuracy of AI may come at a cost of losing explainability (to patients and clinicians) on how the technology works.; (Copyright © 2020 by the American Association for the Study of Liver Diseases.) |
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| Entry Date(s): | Date Created: 20200411 Date Completed: 20210318 Latest Revision: 20250522 |
| Update Code: | 20260130 |
| DOI: | 10.1002/lt.25772 |
| PMID: | 32274856 |
| Database: | MEDLINE |
Journal Article; Systematic Review