Applications of Artificial Intelligence in Kidney Transplantation: A Scoping Review across the Continuum of CareApplications of Artificial Intelligence in Kidney Transplantation
Keywords:
Artificial intelligence, Machine learning, Scoping review, Kidney TransplantationAbstract
Objectives: To map the scientific literature on the application of artificial intelligence (AI) in the kidney transplantation process, identifying its main uses, the stages of care in which these applications are concentrated, and the outcomes and processes they aim to influence. Methods: A scoping review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. The search was performed in the Scopus database using terms related to AI and kidney transplantation, covering publications from 2019 to 2023. Peer-reviewed empirical studies applying AI at any stage of the kidney transplantation process were included. Study selection followed a screening and eligibility process, and the included studies were analyzed using inductive content analysis with iterative categorization of applications, stages of care, and investigated outcomes. Results: A total of 181 studies were included. AI applications were organized into seven categories: prediction of patient behavior; radiological and pathological assessment; prediction of pre-transplant kidney disease progression; prediction of donor-recipient compatibility; optimization of immunosuppressive drug administration; diagnosis of post-transplant complications; and prediction of graft survival. Applications were predominantly concentrated in the post-transplant phase (65.7%), followed by studies spanning both pre- and post-transplant phases (24.3%), while the pre-transplant phase accounted for 9.9% of publications. Overall, the studies aimed to influence outcomes related to early detection of complications, risk stratification, therapeutic optimization, and graft survival. Conclusion: The literature shows growing and heterogeneous applications of AI in kidney transplantation, with a strong focus on diagnostic and predictive tasks in the post-transplant period and important gaps in the pre-transplant phase and in behavioral dimensions of care. This mapping provides an integrated overview of the field and may support researchers, clinicians, and healthcare managers in identifying opportunities for the development and implementation of AI-based solutions across the kidney transplantation care continuum.
Downloads
References
1. Nemati M, Zhang H, Sloma M, Bekbolsynov D, Wang H, Stepkowski S, et al. Predicting kidney transplant survival using multiple feature representations for HLAs. Artif Intell Med, 2023;145. https://doi.org/10.1016/j.artmed.2023.102675
2. Lee J, Silva MB, Bao Y, Whitmarsh R, Banerjee S, O'Connor J, et al. Performance and advancement of the kidney solid organ response test. Transplantation, 2023;107(10):2271-8. https://doi.org/10.1097/TP.0000000000004690
3. Silva SB, Caulliraux HM, Araujo CA, Rocha E. Cost comparison of kidney transplant versus dialysis in Brazil. Cad Saude Publica, 2016;32(6). https://doi.org/10.1590/0102-311X00013515
4. Almeida J, Araujo CA, Roza BDA, Siqueira MM, Rocha E. Risk analysis of the organ donation-transplantation process in Brazil. Transplant Proc, 2021;53(2). https://doi.org/10.1016/j.transproceed.2021.01.018
5. Lai X, Zheng X, Mathew JM, Gallon L, Leventhal JR, Zhang ZJ. Tackling chronic kidney transplant rejection: challenges and promises. Front Immunol, 2021;12. https://doi.org/10.3389/fimmu.2021.661643
6. Dasariraju S, Gragert L, Wager GL, McCullough K, Brown NK, Kamoun M, et al. HLA amino acid mismatch-based risk stratification of kidney allograft failure using a novel machine learning algorithm. J Biomed Inform, 2023;142. https://doi.org/10.1016/j.jbi.2023.104374
7. Minato ACDS, Hannun PGC, Barbosa AMP, da Rocha NC, Machado-Rugolo J, Cardoso MMDA, et al. Machine learning model to predict graft rejection after kidney transplantation. Transplant Proc, 2023;55(9):2058-62. https://doi.org/10.1016/j.transproceed.2023.07.021
8. Miao J, Thongprayoon C, Suppadungsuk S, Garcia Valencia OA, Qureshi F, Cheungpasitporn W. Innovating personalized nephrology care: exploring the potential utilization of ChatGPT. J Pers Med, 2023;13(12):1681. https://doi.org/10.3390/jpm13121681
9. Gotlieb N, Azhie A, Sharma D, Spann A, Suo NJ, Tran J, et al. The promise of machine learning applications in solid organ transplantation. npj Digit Med, 2022;5(1):89. https://doi.org/10.1038/s41746-022-00637-2
10. Park S, Park BS, Lee YJ, Kim IH, Park JH, Ko J, et al. Artificial intelligence with kidney disease: a scoping review with bibliometric analysis, PRISMA-ScR. Medicine (Baltimore), 2021; 100(14): e25422. https://doi.org/10.1097/MD.0000000000025422
11. Loftus TJ, Shickel B, Ozrazgat-Baslanti T, Ren Y, Glicksberg BS, Cao J, et al. Artificial intelligence-enabled decision support in nephrology. Nat Rev Nephrol, 2022;18(7):452-65. https://doi.org/10.1038/s41581-022-00562-3
12. Bajaj T, Koyner JL. Artificial intelligence in acute kidney injury prediction. Adv Chronic Kidney Dis, 2022; 29(5): 450-60. https://doi.org/10.1053/j.ackd.2022.07.009
13. Díez-Sanmartín C, Cabezuelo AS. Application of artificial intelligence techniques to predict survival in kidney transplantation: a review. J Clin Med, 2020; 9(2). https://doi.org/10.3390/jcm9020572
14. Sekercioglu N, Fu R, Kim SJ, Mitsakakis N. Machine learning for predicting long-term kidney allograft survival: a scoping review. Ir J Med Sci, 2021; 190(2): 807-17. https://doi.org/10.1007/s11845-020-02332-1
15. Díez-Sanmartín C, Sarasa-Cabezuelo A, Belmonte AA. The impact of artificial intelligence and big data on end-stage kidney disease treatments. Expert Syst Appl, 2021;180. https://doi.org/10.1016/j.eswa.2021.115076
16. Aslani N, Galehdar N, Garavand A. A systematic review of data mining applications in kidney transplantation. Inform Med Unlocked, 2023; 37. https://doi.org/10.1016/j.imu.2023.101165
17. Burlacu A, Iftene A, Jugrin D, Popa IV, Lupu PM, Vlad C, et al. Using artificial intelligence resources in dialysis and kidney transplant patients: a literature review. Biomed Res Int, 2020; 2020. https://doi.org/10.1155/2020/9867872
18. Farris AB, Vizcarra J, Amgad M, Cooper LAD, Gutman D, Hogan J. Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples. Histopathology, 2021; 78(6): 791-804. https://doi.org/10.1111/his.14304
19. Bullow RD, Marsh JN, Swamidass SJ, Gaut JP, Boor P. The potential of artificial intelligence-based applications in kidney pathology. Curr Opin Nephrol Hypertens, 2022; 31(3): 251-7. https://doi.org/10.1097/MNH.0000000000000784
20. Dominy CL, Shamsian EB, Okhawere KE, Korn TG, Meilika K, Badani K. Recent innovations in renal replacement technology and potential applications to transplantation and dialysis patients: a review of current methods. Kidney Res Clin Pract, 2023; 42(1): 53-62. https://doi.org/10.23876/j.krcp.22.074
21. Pham P-T, Pham P-A, Pham P-C, Parikh S, Danovitch G. Evaluation of adult kidney transplant candidates. Semin Dial, 2010; 23(6): 595-605. https://doi.org/10.1111/j.1525-139X.2010.00809.x
22. Ghanta M, Jim B. Renal transplantation in advanced chronic kidney disease patients. Med Clin North Am, 2016; 100(3): 465-76. https://doi.org/10.1016/j.mcna.2015.12.003
23. Foley DP, Sawinski D. Personalizing donor kidney selection: choosing the right donor for the right recipient. Clin J Am Soc Nephrol, 2020; 15(3): 418-20. https://doi.org/10.2215/CJN.09180819
24. Chadban SJ, Ahn C, Axelrod DA, Foster BJ, Kasiske BL, Kher V, et al. KDIGO clinical practice guideline on the evaluation and management of candidates for kidney transplantation. Transplantation, 2020; 104(4S1): S11-S103. https://doi.org/10.1097/TP.0000000000003136
25. Sullivan C, Leon JB, Sayre SS, Marbury M, Ivers M, Pencak JA, et al. Impact of navigators on completion of steps in the kidney transplant process: a randomized, controlled trial. Clin J Am Soc Nephrol, 2012; 7(10): 1639-45. https://doi.org/10.2215/CJN.11731111
26. Legendre C, Canaud G, Martinez F. Factors influencing long-term outcome after kidney transplantation. Transpl Int, 2014; 27(1): 19-27. https://doi.org/10.1111/tri.12217
27. Jamieson NJ, Hanson CS, Josephson MA, Gordon EJ, Craig JC, Halleck F, et al. Motivations, challenges, and attitudes to selfmanagement in kidney transplant recipients: a systematic review of qualitative studies. Am J Kidney Dis, 2016; 67(3): 461-78. https://doi.org/10.1053/j.ajkd.2015.07.030
28. Sawinski D, Poggio ED. Introduction to kidney transplantation: long-term management challenges. Clin J Am Soc Nephrol, 2021; 16(8): 1262-3. https://doi.org/10.2215/CJN.13440820
29. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMAScR): checklist and explanation. Ann Intern Med, 2018; 169(7): 467-73. https://doi.org/10.7326/M18-0850
30. Sucharew H, Macaluso M. Methods for research evidence synthesis: the scoping review approach. J Hosp Med, 2019; 14(7): 416-8. https://doi.org/10.12788/jhm.3248
31. Lockwood C, dos Santos KB, Pap R. Practical guidance for knowledge synthesis: scoping review methods. Asian Nurs Res, 2019; 13(5): 287-94. https://doi.org/10.1016/j.anr.2019.11.002
32. Peters MD, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Synth, 2020; 18(10): 2119-26. https://doi.org/10.11124/JBIES-20-00167
33. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 2021; 372. https://doi.org/10.1136/bmj.n71
34. Elo S, Kyngäs H. The qualitative content analysis process. J Adv Nurs, 2008; 62(1): 107-15. https://doi.org/10.1111/j.1365-2648.2007.04569.x
35. Gheyle N, Jacobs T. Content analysis: a short overview. Leuven: KU Leuven; 2017. https://doi.org/10.13140/RG.2.2.33689.31841
36. Aljurbua R, Gillespie A, Obradovic Z. The company we keep: using hemodialysis social network data to classify patients' kidney transplant attitudes with machine learning algorithms. BMC Nephrol, 2022; 23(1): 414. https://doi.org/10.1186/s12882-022-03049-2
37. Zhu X, Peng B, Yi Q, Liu J, Yan J. Prediction model of immunosuppressive medication non-adherence for renal transplant patients based on machine learning technology. Front Med (Lausanne), 2022; 9: 796424. https://doi.org/10.3389/fmed.2022.796424
38. Jaugey A, Maréchal E, Tarris G, Paindavoine M, Martin L, Chabannes M, et al. Deep learning automation of MEST-C classification in IgA nephropathy. Nephrol Dial Transplant, 2023; 38(7): 1741-51. https://doi.org/10.1093/ndt/gfad039
39. Fang F, Liu P, Song L, Wagner P, Bartlett D, Ma L, et al. Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning. Front Immunol, 2023; 14: 1090373. https://doi.org/10.3389/fimmu.2023.1090373
40. Liang P, Yang J, Wang W, Yuan G, Han M, Zhang Q, et al. Deep learning identifies intelligible predictors of poor prognosis in chronic kidney disease. IEEE J Biomed Health Inform, 2023; 27(7): 3677-85. https://doi.org/10.1109/JBHI.2023.3266587
41. Mark E, Goldsman D, Gurbaxani B, Keskinocak P, Sokol J. Predicting a kidney transplant patient’s pre-transplant functional status based on information from waitlist registration. Sci Rep, 2023; 13(1): 6164. https://doi.org/10.1038/s41598-023-33117-y
42. Sridharan K, Shah S. Developing supervised machine learning algorithms to evaluate the therapeutic effect and laboratoryrelated adverse events of cyclosporine and tacrolimus in renal transplants. Int J Clin Pharm, 2023 ;45(3): 659-68. https://doi.org/10.1007/s11096-023-01545-5
43. Sánchez-Herrero S, Calvet L, Juan AA. Machine learning models for predicting personalized tacrolimus stable dosages in pediatric renal transplant patients. BioMedInformatics, 2023; 3(4): 926-47. https://doi.org/10.3390/biomedinformatics3040057
44. Thongprayoon C, Tangpanithandee S, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, et al. Characteristics of kidney transplant recipients with prolonged pre-transplant dialysis duration as identified by machine learning consensus clustering: pathway to personalized care. J Pers Med, 2023; 13(8): 1273. https://doi.org/10.3390/jpm13081273
45. Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, et al. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int, 2023; 103(5): 936-48. https://doi.org/10.1016/j.kint.2022.12.011
46. Badrouchi S, Bacha MM, Ahmed A, Ben Abdallah T, Abderrahim E. Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence. Sci Rep, 2023; 13(1): 21273. https://doi.org/10.1038/s41598-023-48645-w
47. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, et al. A review of the role of artificial intelligence in healthcare. J Pers Med, 2023; 13(6). https://doi.org/10.3390/jpm13060951
48. Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial intelligence applications in health care practice:scoping review. J Med Internet Res, 2022; 24(10): e40238. https://doi.org/10.2196/40238
49. Rong G, Mendez A, Bou Assi E, Zhao B, Sawan M. Artificial intelligence in healthcare: review and prediction case studies. Engineering, 2020; 6(3): 291-301. https://doi.org/10.1016/j.eng.2019.08.015
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bruno Fernandes, Claudia Affonso Silva Araujo, Isabela Braga de Moura
This work is licensed under a Creative Commons Attribution 4.0 International License.
