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Machine learning and artificial intelligence in cardiac transplantation: A systematic review
BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendati...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9545856/ https://www.ncbi.nlm.nih.gov/pubmed/35719121 http://dx.doi.org/10.1111/aor.14334 |
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author | Naruka, Vinci Arjomandi Rad, Arian Subbiah Ponniah, Hariharan Francis, Jeevan Vardanyan, Robert Tasoudis, Panagiotis Magouliotis, Dimitrios E. Lazopoulos, George L. Salmasi, Mohammad Yousuf Athanasiou, Thanos |
author_facet | Naruka, Vinci Arjomandi Rad, Arian Subbiah Ponniah, Hariharan Francis, Jeevan Vardanyan, Robert Tasoudis, Panagiotis Magouliotis, Dimitrios E. Lazopoulos, George L. Salmasi, Mohammad Yousuf Athanasiou, Thanos |
author_sort | Naruka, Vinci |
collection | PubMed |
description | BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. METHODS: A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021. RESULTS: Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk. CONCLUSION: ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices. |
format | Online Article Text |
id | pubmed-9545856 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95458562022-10-14 Machine learning and artificial intelligence in cardiac transplantation: A systematic review Naruka, Vinci Arjomandi Rad, Arian Subbiah Ponniah, Hariharan Francis, Jeevan Vardanyan, Robert Tasoudis, Panagiotis Magouliotis, Dimitrios E. Lazopoulos, George L. Salmasi, Mohammad Yousuf Athanasiou, Thanos Artif Organs Systematic Review BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation. METHODS: A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021. RESULTS: Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk. CONCLUSION: ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices. John Wiley and Sons Inc. 2022-06-20 2022-09 /pmc/articles/PMC9545856/ /pubmed/35719121 http://dx.doi.org/10.1111/aor.14334 Text en © 2022 The Authors. Artificial Organs published by International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Systematic Review Naruka, Vinci Arjomandi Rad, Arian Subbiah Ponniah, Hariharan Francis, Jeevan Vardanyan, Robert Tasoudis, Panagiotis Magouliotis, Dimitrios E. Lazopoulos, George L. Salmasi, Mohammad Yousuf Athanasiou, Thanos Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
title | Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
title_full | Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
title_fullStr | Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
title_full_unstemmed | Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
title_short | Machine learning and artificial intelligence in cardiac transplantation: A systematic review |
title_sort | machine learning and artificial intelligence in cardiac transplantation: a systematic review |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9545856/ https://www.ncbi.nlm.nih.gov/pubmed/35719121 http://dx.doi.org/10.1111/aor.14334 |
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