Advancements in Artificial Intelligence for Kidney Transplantology: A Comprehensive Review of Current Applications and Predictive Models

Overview

Journal J Clin Med

Specialty General Medicine

Date 2025 Feb 13

PMID 39941645

Authors

Jakub Mizera

Maciej Pondel

Marta Kepinska

Patryk Jerzak

Miroslaw Banasik

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence is rapidly advancing within the domains of medicine and transplantology. In this comprehensive review, we provide an in-depth exploration of current AI methodologies, with a particular emphasis on machine learning and deep learning techniques, and their diverse subtypes. These technologies are revolutionizing how data are processed, analyzed, and applied in clinical decision making. A meticulous literature review was conducted with a focus on the application of artificial intelligence in kidney transplantation. Four research questions were formulated to establish the aim of the review. We thoroughly examined the general applications of AI in the medical field, such as feature selection, dimensionality reduction, and clustering, which serve as foundational tools for complex data analysis. This includes the development of predictive models for transplant rejection, the optimization of personalized immunosuppressive therapies, the algorithmic matching of donors and recipients based on multidimensional criteria, and the sophisticated analysis of histopathological images to improve the diagnostic accuracy. Moreover, we present a detailed comparison of existing AI-based algorithms designed to predict kidney graft survival in transplant recipients. In this context, we focus on the variables incorporated into these predictive models, providing a critical analysis of their relative importance and contribution to model performance. This review highlights the significant advancements made possible through AI and underscores its potential to enhance both clinical outcomes and the precision of medical interventions in the field of transplantology.

References

Parsons R, Locke J, Redfield 3rd R, Roll G, Levine M . Kidney transplantation of highly sensitized recipients under the new kidney allocation system: A reflection from five different transplant centers across the United States. Hum Immunol. 2016; 78(1):30-36. PMC: 5554113. DOI: 10.1016/j.humimm.2016.10.009. View

Mark E, Goldsman D, Gurbaxani B, Keskinocak P, Sokol J . Using machine learning and an ensemble of methods to predict kidney transplant survival. PLoS One. 2019; 14(1):e0209068. PMC: 6326487. DOI: 10.1371/journal.pone.0209068. View

Yang Z . FCM Clustering on Interaction Pattern Analysis of Chinese Language Learner Behavior. Comput Intell Neurosci. 2022; 2022:8256646. PMC: 9200542. DOI: 10.1155/2022/8256646. View

Vagliano I, Chesnaye N, Leopold J, Jager K, Abu-Hanna A, Schut M . Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal. Clin Kidney J. 2022; 15(12):2266-2280. PMC: 9664575. DOI: 10.1093/ckj/sfac181. View

Mizera J, Pilch J, Giordano U, Krajewska M, Banasik M . Therapy in the Course of Kidney Graft Rejection-Implications for the Cardiovascular System-A Systematic Review. Life (Basel). 2023; 13(7). PMC: 10381422. DOI: 10.3390/life13071458. View

Kers J, Bulow R, Klinkhammer B, Breimer G, Fontana F, Abiola A . Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study. Lancet Digit Health. 2021; 4(1):e18-e26. DOI: 10.1016/S2589-7500(21)00211-9. View

Hart A, Schladt D, Matas A, Itzler R, Israni A, Kasiske B . Incidence, risk factors, and long-term outcomes associated with antibody-mediated rejection - The long-term Deterioration of Kidney Allograft Function (DeKAF) prospective cohort study. Clin Transplant. 2021; 35(7):e14337. DOI: 10.1111/ctr.14337. View

Lee C, Yang C, Lin W, Chen C, Tsai M . Prognostic factors for renal transplant graft survival in a retrospective cohort of 1000 cases: The role of desensitization therapy. J Formos Med Assoc. 2019; 119(4):829-837. DOI: 10.1016/j.jfma.2019.11.008. View

Weinrauch L, Anis K, DElia J . Diabetes and the solid organ transplant recipient. Diabetes Res Clin Pract. 2018; 146:220-224. DOI: 10.1016/j.diabres.2018.10.015. View

10.

Fu Q, Jing Y, Liu Mr G, Jiang Mr X, Liu H, Kong Y . Machine learning-based method for tacrolimus dose predictions in Chinese kidney transplant perioperative patients. J Clin Pharm Ther. 2021; 47(5):600-608. DOI: 10.1111/jcpt.13579. View

11.

Goren S, Karahoca A, Onat F, Goren M . Prediction of cyclosporine A blood levels: an application of the adaptive-network-based fuzzy inference system (ANFIS) in assisting drug therapy. Eur J Clin Pharmacol. 2008; 64(8):807-14. DOI: 10.1007/s00228-008-0490-x. View

12.

Koukoulaki M, Kitsiou V, Balaska A, Pistolas D, Loukopoulos I, Drakopoulos V . Immunologic prognostic factors of renal allograft survival. Transplant Proc. 2014; 46(9):3175-8. DOI: 10.1016/j.transproceed.2014.10.031. View

13.

Schardt C, Adams M, Owens T, Keitz S, Fontelo P . Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak. 2007; 7:16. PMC: 1904193. DOI: 10.1186/1472-6947-7-16. View

14.

Qiao N . A systematic review on machine learning in sellar region diseases: quality and reporting items. Endocr Connect. 2019; 8(7):952-960. PMC: 6612064. DOI: 10.1530/EC-19-0156. View

15.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

16.

Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M . Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int. 2022; 103(5):936-948. DOI: 10.1016/j.kint.2022.12.011. View

17.

Naqvi S, Tennankore K, Vinson A, Roy P, Abidi S . Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study. J Med Internet Res. 2021; 23(8):e26843. PMC: 8433864. DOI: 10.2196/26843. View

18.

Page M, McKenzie J, Bossuyt P, Boutron I, Hoffmann T, Mulrow C . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372:n71. PMC: 8005924. DOI: 10.1136/bmj.n71. View

19.

Rao P, Schaubel D, Guidinger M, Andreoni K, Wolfe R, Merion R . A comprehensive risk quantification score for deceased donor kidneys: the kidney donor risk index. Transplantation. 2009; 88(2):231-6. DOI: 10.1097/TP.0b013e3181ac620b. View

20.

Malik M, Akoh J, Houlberg K . Exploring the Risk Factors and Preventable Measures for Early Graft Loss in Kidney Transplantation: A Single-Center Analysis in the Southwest of the United Kingdom. Exp Clin Transplant. 2023; 21(8):639-644. DOI: 10.6002/ect.2023.0099. View