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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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“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]
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“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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Metadata
- 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
- 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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