Research advances in the application of artificial intelligence for the diagnosis and treatment of acute kidney injury_West China Medical Journal

Authors：

WEI Wei ,  FU Ping

Department of Nephrology and Institute of Kidney Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

FU Ping, Email: fupinghx@vip.163.com

Keywords：

Artificial intelligence; acute kidney injury; diagnosis; treatment

DOI：

10.7507/1002-0179.202507136

Video：

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Acute kidney injury (AKI) is a common critical illness in clinical practice, with complex etiologies, acute onset, and rapid progression. It not only significantly increases the mortality rate of patients, but also may progress to chronic kidney disease. Currently, its incidence remains high, and improving early diagnosis rate and treatment efficacy is a major clinical challenge. Artificial intelligence (AI), with its powerful data processing and analysis capabilities, is developing rapidly in medical field, providing new ideas for disease diagnosis and treatment, and showing great potential in revolutionizing the early diagnosis, condition assessment, and treatment decision-making models in the AKI field. This article will review the application progress of AI in AKI prediction, condition assessment, and treatment decision-making, so as to provide references for clinicians and promote the further application and development of AI in the AKI field.

1.	Ostermann M, Lumlertgul N, Jeong R, et al. Acute kidney injury. Lancet, 2025, 405(10474): 241-256.
2.	Matsuura R, Doi K, Rabb H. Acute kidney injury and distant organ dysfunction-network system analysis. Kidney Int, 2023, 103(6): 1041-1055.
3.	Levey AS, James MT. Acute kidney injury. Ann Intern Med, 2017, 167(9): ITC66-ITC80.
4.	Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
5.	Bajaj T, Koyner JL. Artificial intelligence in acute kidney injury prediction. Adv Chronic Kidney Dis, 2022, 29(5): 450-460.
6.	Gottlieb ER, Samuel M, Bonventre JV, et al. Machine learning for acute kidney injury prediction in the intensive care unit. Adv Chronic Kidney Dis, 2022, 29(5): 431-438.
7.	Choi SC, Muizelaar JP, Barnes TY, et al. Prediction tree for severely head-injured patients. J Neurosurg, 1991, 75(2): 251-255.
8.	Zelic I, Kononenko I, Lavrac N, et al. Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries. J Med Syst, 1997, 21(6): 429-444.
9.	Ghiasi MM, Zendehboudi S. Application of decision tree-based ensemble learning in the classification of breast cancer. Comput Biol Med, 2021, 128: 104089.
10.	He J, Lin J, Duan M. Application of machine learning to predict acute kidney disease in patients with sepsis associated acute kidney injury. Front Med (Lausanne), 2021, 8: 792974.
11.	Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet, 2019, 394(10212): 1949-1964.
12.	Xin W, Yi W, Liu H, et al. Early prediction of acute kidney injury after liver transplantation by scoring system and decision tree. Ren Fail, 2021, 43(1): 1137-1145.
13.	Cai D, Xiao T, Zou A, et al. Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med, 2022, 9: 964894.
14.	Wang J, Chu H, Pan Y. Prediction of renal damage in children with IgA vasculitis based on machine learning. Medicine (Baltimore), 2022, 101(42): e31135.
15.	Tong Y, Niu X, Liu F. Prediction of acute kidney injury after extracorporeal cardiac surgery (CSA-AKI) by machine learning algorithms. Heart Surg Forum, 2023, 26(5): E537-E551.
16.	Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
17.	Koyner JL, Carey KA, Edelson DP, et al. The development of a machine learning inpatient acute kidney injury prediction model. Crit Care Med, 2018, 46(7): 1070-1077.
18.	Sun R, Li S, Wei Y, et al. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study. Int J Surg, 2024, 110(5): 2950-2962.
19.	Xu L, Jiang S, Li C, et al. Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach. Ren Fail, 2024, 46(2): 2438858.
20.	Wei S, Zhang Y, Dong H, et al. Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome. BMC Pulm Med, 2023, 23(1): 370.
21.	Alfieri F, Ancona A, Tripepi G, et al. A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients. J Nephrol, 2021, 34(6): 1875-1886.
22.	Rank N, Pfahringer B, Kempfert J, et al. Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. NPJ Digit Med, 2020, 3: 139.
23.	Le S, Allen A, Calvert J, et al. Convolutional neural network model for intensive care unit acute kidney injury prediction. Kidney Int Rep, 2021, 6(5): 1289-1298.
24.	Kate RJ, Pearce N, Mazumdar D, et al. A continual prediction model for inpatient acute kidney injury. Comput Biol Med, 2020, 116: 103580.
25.	Rashidi HH, Makley A, Palmieri TL, et al. Enhancing military burn- and trauma-related acute kidney injury prediction through an automated machine learning platform and point-of-care testing. Arch Pathol Lab Med, 2021, 145(3): 320-326.
26.	He L, Zhang Q, Li Z, et al. Incorporation of urinary neutrophil gelatinase-associated lipocalin and computed tomography quantification to predict acute kidney injury and in-hospital death in COVID-19 patients. Kidney Dis (Basel), 2021, 7(2): 120-130.
27.	Peng C, Yang F, Li L, et al. A machine learning approach for the prediction of severe acute kidney injury following traumatic brain injury. Neurocrit Care, 2023, 38(2): 335-344.
28.	Neyra JA, Ortiz-Soriano V, Liu LJ, et al. Prediction of mortality and major adverse kidney events in critically ill patients with acute kidney injury. Am J Kidney Dis, 2023, 81(1): 36-47.
29.	Li X, Wu R, Zhao W, et al. Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury. Sci Rep, 2023, 13(1): 5223.
30.	Gao T, Nong Z, Luo Y, et al. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury. Ren Fail, 2024, 46(1): 2316267.
31.	Liu Y, Zhu X, Xue J, et al. Machine learning models for mortality prediction in critically ill patients with acute pancreatitis-associated acute kidney injury. Clin Kidney J, 2024, 17(10): sfae284.
32.	Wei W, Cai Z, Chen L, et al. Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning. BMC Med Inform Decis Mak, 2023, 23(1): 133.
33.	Zhang Z, Ho KM, Hong Y. Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care. Crit Care, 2019, 23(1): 112.
34.	Jiang M, Pan CQ, Li J, et al. Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury. Ren Fail, 2023, 45(1): 2151468.
35.	Chambers P, Watson M, Bridgewater J, et al. Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Med, 2023, 12(17): 17856-17865.

1. Ostermann M, Lumlertgul N, Jeong R, et al. Acute kidney injury. Lancet, 2025, 405(10474): 241-256.
2. Matsuura R, Doi K, Rabb H. Acute kidney injury and distant organ dysfunction-network system analysis. Kidney Int, 2023, 103(6): 1041-1055.
3. Levey AS, James MT. Acute kidney injury. Ann Intern Med, 2017, 167(9): ITC66-ITC80.
4. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism, 2017, 69S: S36-S40.
5. Bajaj T, Koyner JL. Artificial intelligence in acute kidney injury prediction. Adv Chronic Kidney Dis, 2022, 29(5): 450-460.
6. Gottlieb ER, Samuel M, Bonventre JV, et al. Machine learning for acute kidney injury prediction in the intensive care unit. Adv Chronic Kidney Dis, 2022, 29(5): 431-438.
7. Choi SC, Muizelaar JP, Barnes TY, et al. Prediction tree for severely head-injured patients. J Neurosurg, 1991, 75(2): 251-255.
8. Zelic I, Kononenko I, Lavrac N, et al. Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries. J Med Syst, 1997, 21(6): 429-444.
9. Ghiasi MM, Zendehboudi S. Application of decision tree-based ensemble learning in the classification of breast cancer. Comput Biol Med, 2021, 128: 104089.
10. He J, Lin J, Duan M. Application of machine learning to predict acute kidney disease in patients with sepsis associated acute kidney injury. Front Med (Lausanne), 2021, 8: 792974.
11. Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet, 2019, 394(10212): 1949-1964.
12. Xin W, Yi W, Liu H, et al. Early prediction of acute kidney injury after liver transplantation by scoring system and decision tree. Ren Fail, 2021, 43(1): 1137-1145.
13. Cai D, Xiao T, Zou A, et al. Predicting acute kidney injury risk in acute myocardial infarction patients: an artificial intelligence model using medical information mart for intensive care databases. Front Cardiovasc Med, 2022, 9: 964894.
14. Wang J, Chu H, Pan Y. Prediction of renal damage in children with IgA vasculitis based on machine learning. Medicine (Baltimore), 2022, 101(42): e31135.
15. Tong Y, Niu X, Liu F. Prediction of acute kidney injury after extracorporeal cardiac surgery (CSA-AKI) by machine learning algorithms. Heart Surg Forum, 2023, 26(5): E537-E551.
16. Tseng PY, Chen YT, Wang CH, et al. Prediction of the development of acute kidney injury following cardiac surgery by machine learning. Crit Care, 2020, 24(1): 478.
17. Koyner JL, Carey KA, Edelson DP, et al. The development of a machine learning inpatient acute kidney injury prediction model. Crit Care Med, 2018, 46(7): 1070-1077.
18. Sun R, Li S, Wei Y, et al. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study. Int J Surg, 2024, 110(5): 2950-2962.
19. Xu L, Jiang S, Li C, et al. Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach. Ren Fail, 2024, 46(2): 2438858.
20. Wei S, Zhang Y, Dong H, et al. Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome. BMC Pulm Med, 2023, 23(1): 370.
21. Alfieri F, Ancona A, Tripepi G, et al. A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients. J Nephrol, 2021, 34(6): 1875-1886.
22. Rank N, Pfahringer B, Kempfert J, et al. Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance. NPJ Digit Med, 2020, 3: 139.
23. Le S, Allen A, Calvert J, et al. Convolutional neural network model for intensive care unit acute kidney injury prediction. Kidney Int Rep, 2021, 6(5): 1289-1298.
24. Kate RJ, Pearce N, Mazumdar D, et al. A continual prediction model for inpatient acute kidney injury. Comput Biol Med, 2020, 116: 103580.
25. Rashidi HH, Makley A, Palmieri TL, et al. Enhancing military burn- and trauma-related acute kidney injury prediction through an automated machine learning platform and point-of-care testing. Arch Pathol Lab Med, 2021, 145(3): 320-326.
26. He L, Zhang Q, Li Z, et al. Incorporation of urinary neutrophil gelatinase-associated lipocalin and computed tomography quantification to predict acute kidney injury and in-hospital death in COVID-19 patients. Kidney Dis (Basel), 2021, 7(2): 120-130.
27. Peng C, Yang F, Li L, et al. A machine learning approach for the prediction of severe acute kidney injury following traumatic brain injury. Neurocrit Care, 2023, 38(2): 335-344.
28. Neyra JA, Ortiz-Soriano V, Liu LJ, et al. Prediction of mortality and major adverse kidney events in critically ill patients with acute kidney injury. Am J Kidney Dis, 2023, 81(1): 36-47.
29. Li X, Wu R, Zhao W, et al. Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury. Sci Rep, 2023, 13(1): 5223.
30. Gao T, Nong Z, Luo Y, et al. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury. Ren Fail, 2024, 46(1): 2316267.
31. Liu Y, Zhu X, Xue J, et al. Machine learning models for mortality prediction in critically ill patients with acute pancreatitis-associated acute kidney injury. Clin Kidney J, 2024, 17(10): sfae284.
32. Wei W, Cai Z, Chen L, et al. Short-term prognostic models for severe acute kidney injury patients receiving prolonged intermittent renal replacement therapy based on machine learning. BMC Med Inform Decis Mak, 2023, 23(1): 133.
33. Zhang Z, Ho KM, Hong Y. Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care. Crit Care, 2019, 23(1): 112.
34. Jiang M, Pan CQ, Li J, et al. Explainable machine learning model for predicting furosemide responsiveness in patients with oliguric acute kidney injury. Ren Fail, 2023, 45(1): 2151468.
35. Chambers P, Watson M, Bridgewater J, et al. Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function. Cancer Med, 2023, 12(17): 17856-17865.

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