Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons
BackgroundThe complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using t...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Surgery |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fsurg.2023.1048451/full |
| _version_ | 1797677119016271872 |
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| author | Beatriz Pontes Balanza Juan M. Castillo Tuñón Daniel Mateos García Javier Padillo Ruiz José C. Riquelme Santos José M. Álamo Martinez Carmen Bernal Bellido Gonzalo Suarez Artacho Carmen Cepeda Franco Miguel A. Gómez Bravo Luis M. Marín Gómez |
| author_facet | Beatriz Pontes Balanza Juan M. Castillo Tuñón Daniel Mateos García Javier Padillo Ruiz José C. Riquelme Santos José M. Álamo Martinez Carmen Bernal Bellido Gonzalo Suarez Artacho Carmen Cepeda Franco Miguel A. Gómez Bravo Luis M. Marín Gómez |
| author_sort | Beatriz Pontes Balanza |
| collection | DOAJ |
| description | BackgroundThe complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.Material and methodLiver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated “in situ” for transplantation, and those discarded after the “in situ” evaluation were considered as no transplantable liver grafts, while those grafts transplanted after “in situ” evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.ResultsA total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85.ConclusionThe tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation. |
| first_indexed | 2024-03-11T22:40:21Z |
| format | Article |
| id | doaj.art-f84dbda27f254bc298ef747754ce07d0 |
| institution | Directory Open Access Journal |
| issn | 2296-875X |
| language | English |
| last_indexed | 2024-03-11T22:40:21Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Surgery |
| spelling | doaj.art-f84dbda27f254bc298ef747754ce07d02023-09-22T09:19:11ZengFrontiers Media S.A.Frontiers in Surgery2296-875X2023-09-011010.3389/fsurg.2023.10484511048451Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeonsBeatriz Pontes Balanza0Juan M. Castillo Tuñón1Daniel Mateos García2Javier Padillo Ruiz3José C. Riquelme Santos4José M. Álamo Martinez5Carmen Bernal Bellido6Gonzalo Suarez Artacho7Carmen Cepeda Franco8Miguel A. Gómez Bravo9Luis M. Marín Gómez10Department of Computer Languages and Systems, Sevilla University, Seville,SpainHPB Surgery Unit, Virgen Macarena University Hospital, Seville,SpainDepartment of Computer Languages and Systems, Sevilla University, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainDepartment of Computer Languages and Systems, Sevilla University, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainHPB Surgery and Liver Transplant Unit, Virgen del Rocío University Hospital, Seville,SpainBackgroundThe complex process of liver graft assessment is one point for improvement in liver transplantation. The main objective of this study is to develop a tool that supports the surgeon who is responsible for liver donation in the decision-making process whether to accept a graft or not using the initial variables available to it.Material and methodLiver graft samples candidate for liver transplantation after donor brain death were studied. All of them were evaluated “in situ” for transplantation, and those discarded after the “in situ” evaluation were considered as no transplantable liver grafts, while those grafts transplanted after “in situ” evaluation were considered as transplantable liver grafts. First, a single-center, retrospective and cohort study identifying the risk factors associated with the no transplantable group was performed. Then, a prediction model decision support system based on machine learning, and using a tree ensemble boosting classifier that is capable of helping to decide whether to accept or decline a donor liver graft, was developed.ResultsA total of 350 liver grafts that were evaluated for liver transplantation were studied. Steatosis was the most frequent reason for classifying grafts as no transplantable, and the main risk factors identified in the univariant study were age, dyslipidemia, personal medical history, personal surgical history, bilirubinemia, and the result of previous liver ultrasound (p < 0.05). When studying the developed model, we observe that the best performance reordering in terms of accuracy corresponds to 76.29% with an area under the curve of 0.79. Furthermore, the model provides a classification together with a confidence index of reliability, for most cases in our data, with the probability of success in the prediction being above 0.85.ConclusionThe tool presented in this study obtains a high accuracy in predicting whether a liver graft will be transplanted or deemed non-transplantable based on the initial variables assigned to it. The inherent capacity for improvement in the system causes the rate of correct predictions to increase as new data are entered. Therefore, we believe it is a tool that can help optimize the graft pool for liver transplantation.https://www.frontiersin.org/articles/10.3389/fsurg.2023.1048451/fullliver transplantsmachine learningdecision-making processliver graft assessmentartificial intelligence |
| spellingShingle | Beatriz Pontes Balanza Juan M. Castillo Tuñón Daniel Mateos García Javier Padillo Ruiz José C. Riquelme Santos José M. Álamo Martinez Carmen Bernal Bellido Gonzalo Suarez Artacho Carmen Cepeda Franco Miguel A. Gómez Bravo Luis M. Marín Gómez Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons Frontiers in Surgery liver transplants machine learning decision-making process liver graft assessment artificial intelligence |
| title | Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons |
| title_full | Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons |
| title_fullStr | Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons |
| title_full_unstemmed | Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons |
| title_short | Development of a liver graft assessment expert machine-learning system: when the artificial intelligence helps liver transplant surgeons |
| title_sort | development of a liver graft assessment expert machine learning system when the artificial intelligence helps liver transplant surgeons |
| topic | liver transplants machine learning decision-making process liver graft assessment artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fsurg.2023.1048451/full |
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