Cargando…
Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review
BACKGROUND: The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms ha...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303737/ https://www.ncbi.nlm.nih.gov/pubmed/32596403 http://dx.doi.org/10.1155/2020/9867872 |
_version_ | 1783548123411382272 |
---|---|
author | Burlacu, Alexandru Iftene, Adrian Jugrin, Daniel Popa, Iolanda Valentina Lupu, Paula Madalina Vlad, Cristiana Covic, Adrian |
author_facet | Burlacu, Alexandru Iftene, Adrian Jugrin, Daniel Popa, Iolanda Valentina Lupu, Paula Madalina Vlad, Cristiana Covic, Adrian |
author_sort | Burlacu, Alexandru |
collection | PubMed |
description | BACKGROUND: The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? METHODS: We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. RESULTS: AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. CONCLUSIONS: Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival. |
format | Online Article Text |
id | pubmed-7303737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-73037372020-06-27 Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review Burlacu, Alexandru Iftene, Adrian Jugrin, Daniel Popa, Iolanda Valentina Lupu, Paula Madalina Vlad, Cristiana Covic, Adrian Biomed Res Int Review Article BACKGROUND: The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? METHODS: We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. RESULTS: AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. CONCLUSIONS: Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival. Hindawi 2020-06-10 /pmc/articles/PMC7303737/ /pubmed/32596403 http://dx.doi.org/10.1155/2020/9867872 Text en Copyright © 2020 Alexandru Burlacu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Review Article Burlacu, Alexandru Iftene, Adrian Jugrin, Daniel Popa, Iolanda Valentina Lupu, Paula Madalina Vlad, Cristiana Covic, Adrian Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review |
title | Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review |
title_full | Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review |
title_fullStr | Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review |
title_full_unstemmed | Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review |
title_short | Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review |
title_sort | using artificial intelligence resources in dialysis and kidney transplant patients: a literature review |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7303737/ https://www.ncbi.nlm.nih.gov/pubmed/32596403 http://dx.doi.org/10.1155/2020/9867872 |
work_keys_str_mv | AT burlacualexandru usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview AT ifteneadrian usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview AT jugrindaniel usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview AT popaiolandavalentina usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview AT lupupaulamadalina usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview AT vladcristiana usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview AT covicadrian usingartificialintelligenceresourcesindialysisandkidneytransplantpatientsaliteraturereview |