Seyahi N, Ozcan SG. Artificial intelligence and kidney transplantation. World J Transplant 2021; 11(7): 277-289 [PMID: 34316452 DOI: 10.5500/wjt.v11.i7.277]
Corresponding Author of This Article
Nurhan Seyahi, MD, Professor, Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Cerrahpaşa Tıp Fakültesi, İç hastalıkları Anabilim Dalı, Istanbul 34098, Fatih, Turkey. nseyahi@yahoo.com
Research Domain of This Article
Transplantation
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Transplant. Jul 18, 2021; 11(7): 277-289 Published online Jul 18, 2021. doi: 10.5500/wjt.v11.i7.277
Artificial intelligence and kidney transplantation
Nurhan Seyahi, Seyda Gul Ozcan
Nurhan Seyahi, Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
Seyda Gul Ozcan, Department of Internal Medicine, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul 34098, Fatih, Turkey
Author contributions: Seyahi N designed the outline and coordinated and performed the writing of the paper; Ozcan SG performed the literature search and writing, and prepared the tables.
Conflict-of-interest statement: The authors declare no conflict of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Nurhan Seyahi, MD, Professor, Department of Nephrology, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Cerrahpaşa Tıp Fakültesi, İç hastalıkları Anabilim Dalı, Istanbul 34098, Fatih, Turkey. nseyahi@yahoo.com
Received: February 28, 2021 Peer-review started: February 28, 2021 First decision: May 5, 2021 Revised: May 17, 2021 Accepted: June 4, 2021 Article in press: June 4, 2021 Published online: July 18, 2021
Abstract
Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.
Core Tip: Artificial intelligence is used in a large spectrum of areas in kidney transplantation. Developments in those areas will shape the future of medical care with faster and more standardized medical evaluations and more accurate personalized judgments.
