Use of Artificial Intelligence as an Innovative Donor-Recipient Matching Model for Liver Transplantation: Results from a Multicenter Spanish Study
Open accessAbstract
Background & aims There is an increasing discrepancy between the number of potential liver graft recipients and the number of organs available. Organ allocation should follow the concept of benefit of survival, avoiding human-innate subjectivity. The aim of this study is to use artificial-neural-networks (ANNs) for donor-recipient (D-R) matching in liver transplantation (LT) and to compare its accuracy with validated scores (MELD, D-MELD, DRI, P-SOFT, SOFT, and BAR) of graft survival.Methods 64 donor and recipient variables from a set of 1003 LTs from a multicenter study including 11 Spanish centres were included. For each D-R pair, common statistics (simple and multiple regression models) and ANN formulae for two non-complementary probability-models of 3-month graft-survival and -loss were calculated: a positive-survival (NN-CCR) and a negative-loss (NN-MS) model. The NN models were obtained by using the Neural Net Evolutionary Programming (NNEP) algorithm. Additionally, receiver-operating-curves (ROC) were performed to validate ANNs against other scores.Results Optimal results for NN-CCR and NN-MS models were obtained, with the best performance in predicting the probability of graft-survival (90.79%) and -loss (71.42%) for each D-R pair, significantly improving results from multiple regressions. ROC curves for 3-months graft-survival and -loss predictions were significantly more accurate for ANN than for other scores in both NN-CCR (AUROC-ANN=0.80 vs. -MELD=0.50; -D-MELD=0.54; -P-SOFT=0.54; -SOFT=0.55; -BAR=0.67 and -DRI=0.42) and NN-MS (AUROC-ANN=0.82 vs. -MELD=0.41; -D-MELD=0.47; -P-SOFT=0.43; -SOFT=0.57, -BAR=0.61 and -DRI=0.48).Conclusions ANNs may be considered a powerful decision-making technology for this dataset, optimizing the principles of justice, efficiency and equity. This may be a useful tool for predicting the 3-month outcome and a potential research area for future D-R matching models.
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Briceño et al. designed a 3-month graft mortality prediction model and performed binary analysis for donor-recipient matching via AI-ANNs.
“In their work, Briceño et al. designed a 3-month graft mortality prediction model based on 64 donor and recipient characteristics and performed a binary analysis (graft survival/loss) for donor-recipient matching via AI-ANNs.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [12]
AI-ANNs' new algorithms predicted graft survival (AUC 0.81) and graft loss (AUC 0.82) better than the isolated donor/recipient scores.
“AI-ANNs’ new algorithms predicted graft survival (AUC, 0.81) and graft loss (AUC, 0.82) better than the isolated donor/recipient scores.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [12]
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Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method
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A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology
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The Role of Artificial Intelligence in Chronic Liver Diseases and Liver Transplantation
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Predicting the 28-day prognosis of acute-on-chronic liver failure patients based on machine learning
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Artificial Intelligence—The Rising Star in the Field of Gastroenterology and Hepatology
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Donor-recipient matching in adult liver transplantation: Current status and advances
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Policy Challenges for Organ Allocation in an Era of “Precision Medicine”
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Application of Machine Learning Model for the Prediction of Settling Velocity of Fine Sediments
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Posttransplant complications: molecular mechanisms and therapeutic interventions
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Machine learning methods in organ transplantation
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Perspectives and Tools in Liver Graft Assessment: A Transformative Era in Liver Transplantation
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Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?
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Self-citationMaximizing utility of nondirected living liver donor grafts using machine learning
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Physiological network approach to prognosis in cirrhosis: A shifting paradigm
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AI Innovations in Liver Transplantation: From Big Data to Better Outcomes
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Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching?
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Transplantation and Alternatives to Treat Autoimmune Diseases
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Ethical frontiers in liver transplantation
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Applications of neural networks in liver transplantation
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Augmenting care in hepatocellular carcinoma with artificial intelligence
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Graft survival after liver transplantation: an approach to a new Spanish risk index
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Artificial neural network and bioavailability of the immunosuppression drug
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Self-citationArtificial neural networks and liver transplantation: Are we ready for self‐driving cars?
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Abdominal Organ Transplantation: Noteworthy Literature in 2020
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Self-citationCurrent state of artificial intelligence in liver transplantation
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The Swiss Transplant Cohort Study: Implications for Transplant Infectious Diseases Research
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- Authors
- Briceño, J., Cruz-Ramírez, M., Prieto, M., Navasa, M., Ortiz de Urbina, J., Orti, R., Gómez-Bravo, M.-Á., Otero, A., Varo, E., Tomé, S., Clemente, G., Bañares, R., Bárcena, R., Cuervas-Mons, V., Solórzano, G., Vinaixa, C., Rubín, Á., Colmenero, J., Valdivieso, A., Ciria, R., Hervás-Martínez, C., & de la Mata, M.
- Affiliations
- Instituto Maimónides de Investigación Biomédica de Córdoba (ES)
- University of Córdoba (ES)
- Hospital Universitari i Politècnic La Fe (ES)
- Hospital Clínic de Barcelona (ES)
- Complejo Hospitalario Universitario de Santiago (ES)
- Hospital General Universitario Gregorio Marañón (ES)
- Hospital Universitario Ramón y Cajal (ES)
- Government of Extremadura (ES)
- Hospital de Cruces (ES)
- Corresponding author
- Briceño, Javier
- ORCID
- Venue
- Journal of Hepatology
- Publisher
- Elsevier BV
- Published
- 2014-11
- Volume
- 61
- Issue
- 5
- Pages
- 1020-1028
- ISSN
- 0168-8278 (ISSN-L)
- OpenAlex
- W2013028137
- Cited by
- 152
- References
- 42
- Citations by year
- 2026: 11 · 2025: 22 · 2024: 19 · 2023: 13 · 2022: 16 · 2021: 17 · 2020: 18 · 2019: 9 · 2018: 7 · 2017: 7 · 2016: 4 · 2015: 6 · 2012: 1
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Journal of Hepatology (publishedVersion)
- PubMed (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- RUNA. Repositorio Institucional del Sergas (Servicio Gallego de Salud (SERGAS)) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Surgery › Organ Transplantation Techniques and Outcomes
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Organ Donation and Transplantation
- Health Sciences › Medicine › Surgery › Transplantation: Methods and Outcomes
- Keywords
- liver transplantation, donor-recipient matching, artificial neural networks, organ allocation, graft survival prediction, graft loss, MELD score, DRI, organ shortage, decision support system
- MeSH terms
- Adolescent, Adult, Aged, Algorithms, Artificial Intelligence, Decision Making, Computer-Assisted, Female, Graft Survival, Humans, Male, Middle Aged, Prognosis, Spain, Tissue Donors, Models, Statistical, Multivariate Analysis, Liver Transplantation, Neural Networks, Computer, Young Adult, Transplant Recipients
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