Aplicaciones de la Inteligencia Artificial en el Trasplante Renal

Autores/as

  • Bruno Fernandes Universidade Federal do Rio de Janeiro – Instituto Coppead de Administração – Rio de Janeiro (RJ) – Brasil. https://orcid.org/0000-0002-1455-0092
  • Claudia Affonso Silva Araujo Universidade Federal do Rio de Janeiro – Instituto Coppead de Administração – Rio de Janeiro (RJ) – Brasil.
  • Isabela Braga de Moura Universidade Federal do Rio de Janeiro – Instituto Coppead de Administração – Rio de Janeiro (RJ) – Brasil. https://orcid.org/0000-0001-9149-0954

Palabras clave:

Trasplante renal, Inteligencia artificial, Aprendizaje automático, Revisión de alcance

Resumen

Objetivo: Mapear la literatura científica sobre aplicaciones de inteligencia artificial (IA) en el proceso de trasplante renal, identificando los principales usos, las etapas del cuidado en las que se concentran y los desenlaces y procesos que buscan influir.

Método: Se realizó una revisión de alcance, reportada conforme a PRISMA-ScR. La búsqueda se llevó a cabo en la base de datos Scopus, utilizando términos relacionados con IA y trasplante renal, abarcando publicaciones entre 2019 y 2023. Se incluyeron estudios empíricos revisados por pares que aplicaran IA en cualquier fase del trasplante renal. La selección se realizó mediante cribado y evaluación de elegibilidad, y los estudios incluidos fueron analizados mediante análisis de contenido inductivo, con categorización iterativa de las aplicaciones, las fases del cuidado y los desenlaces investigados.

Resultados: Se incluyeron 181 estudios. Las aplicaciones de IA se organizaron en siete categorías: predicción de comportamientos del paciente; evaluación radiológica y patológica; predicción de la progresión de la enfermedad renal en la fase pretrasplante; predicción de compatibilidad donante–receptor; optimización de la administración de medicamentos inmunosupresores; diagnóstico de complicaciones postrasplante; y predicción de la supervivencia del injerto. Se observó predominio de aplicaciones en el período postrasplante (65,7%), seguido por estudios que abarcan fases pre y postrasplante (24,3%); la fase pretrasplante concentró el 9,9% de las publicaciones. En general, los estudios buscaron influir en desenlaces relacionados con la detección precoz de complicaciones, la estratificación de riesgo, la optimización terapéutica y la supervivencia del injerto.

Conclusiones: La literatura evidencia crecimiento y heterogeneidad en las aplicaciones de IA en el trasplante renal, con fuerte concentración en tareas diagnósticas y predictivas en el período postrasplante y vacíos relevantes en la fase pretrasplante y en dimensiones conductuales del cuidado. El mapeo ofrece una visión integrada del campo y puede apoyar a investigadores, clínicos y gestores en la identificación de oportunidades para el desarrollo y la incorporación de soluciones de IA a lo largo del continuo del trasplante renal.

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Citas

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

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Publicado

2026-03-23

Cómo citar

1.
Fernandes B, Araujo CAS, Moura IB de. Aplicaciones de la Inteligencia Artificial en el Trasplante Renal. bjt [Internet]. 23 de marzo de 2026 [citado 20 de octubre de 2026];29. Disponible en: https://bjt.emnuvens.com.br/revista/article/view/790

Número

Vol. 29 (2026)

Sección

Artículo de Revisión

Licencia

Derechos de autor 2026 Bruno Fernandes, Claudia Affonso Silva Araujo, Isabela Braga de Moura

Creative Commons License

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.