Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching?
Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-11-01
|
| Series: | Medicina |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1648-9144/58/12/1743 |
| _version_ | 1797456487447003136 |
|---|---|
| author | Rafael Calleja Lozano César Hervás Martínez Francisco Javier Briceño Delgado |
| author_facet | Rafael Calleja Lozano César Hervás Martínez Francisco Javier Briceño Delgado |
| author_sort | Rafael Calleja Lozano |
| collection | DOAJ |
| description | Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered “unbalanced.” In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D–R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution. |
| first_indexed | 2024-03-09T16:07:38Z |
| format | Article |
| id | doaj.art-e20abebff8dd46a18b4d9de6b7ffc845 |
| institution | Directory Open Access Journal |
| issn | 1010-660X 1648-9144 |
| language | English |
| last_indexed | 2024-03-09T16:07:38Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medicina |
| spelling | doaj.art-e20abebff8dd46a18b4d9de6b7ffc8452023-11-24T16:31:57ZengMDPI AGMedicina1010-660X1648-91442022-11-015812174310.3390/medicina58121743Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching?Rafael Calleja Lozano0César Hervás Martínez1Francisco Javier Briceño Delgado2Liver Transplantation Unit, General and Digestive Surgery Department, Reina Sofía University Hospital, 14004 Cordoba, SpainDepartment of Computer Sciences and Numerical Analysis, Universidad de Córdoba, 14014 Cordoba, SpainLiver Transplantation Unit, General and Digestive Surgery Department, Reina Sofía University Hospital, 14004 Cordoba, SpainLiver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor–recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered “unbalanced.” In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D–R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.https://www.mdpi.com/1648-9144/58/12/1743donor–recipient matchingartificial intelligencedeep learningartificial neural networksrandom forestliver transplantation outcomes |
| spellingShingle | Rafael Calleja Lozano César Hervás Martínez Francisco Javier Briceño Delgado Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching? Medicina donor–recipient matching artificial intelligence deep learning artificial neural networks random forest liver transplantation outcomes |
| title | Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching? |
| title_full | Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching? |
| title_fullStr | Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching? |
| title_full_unstemmed | Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching? |
| title_short | Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor–Recipient Matching? |
| title_sort | crossroads in liver transplantation is artificial intelligence the key to donor recipient matching |
| topic | donor–recipient matching artificial intelligence deep learning artificial neural networks random forest liver transplantation outcomes |
| url | https://www.mdpi.com/1648-9144/58/12/1743 |
| work_keys_str_mv | AT rafaelcallejalozano crossroadsinlivertransplantationisartificialintelligencethekeytodonorrecipientmatching AT cesarhervasmartinez crossroadsinlivertransplantationisartificialintelligencethekeytodonorrecipientmatching AT franciscojavierbricenodelgado crossroadsinlivertransplantationisartificialintelligencethekeytodonorrecipientmatching |