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Abstract
Introduction: The introduction of artificial intelligence (AI) into the field of nephrology offers a new perspective for analysing technology-mediated real-time data.
Objectives: To determine the applications of artificial intelligence in nephrology nursing practice and to characterise predictive, diagnostic, and clinical management tools aimed at patients with kidney disease.
Methodology: AWe conducted an integrative literature review in accordance with the PRISMA statement. Original articles with no time restriction were searched in MEDLINE, EBSCO, Cochrane, and LILACS using combinations of terms related to artificial intelligence, nursing, and nephrology. Observational studies, experimental studies, and clinical trials in adult populations published in English, Spanish, or Portuguese were included. Excluded were robotic developments, gynaecological–obstetric patients, and previous reviews. Two reviewers independently extracted data on study design, sample, interventions, comparators, and main outcomes, applying CASPe guidelines to assess methodological quality.
Results: From 279 initial records, 30 studies met the inclusion criteria. They were grouped into 2 categories: 16 studies on predictive and diagnostic tools, and 14 on improved care and clinical management (patient classification systems, early warning systems, dialysis optimisation, and readmission prevention). Most demonstrated the superiority of machine learning and deep learning models compared with traditional approaches.
Conclusions: AI applied to nephrology nursing shows promising performance in prediction and diagnosis, as well as in the optimisation of care processes. Clinical implementation studies and cost-effectiveness evaluations are needed to consolidate its integration into daily practice and maximise its benefits.
Keywords
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Copyright (c) 2025 Dra Claudia Rodriguez, Enfermero Especialista, Enfermera Especialista

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References
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- Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10-8. https://doi.org/10.1177/0840470419873123 DOI: https://doi.org/10.1177/0840470419873123
- Martínez García DN, Dalgo Flores VM, Herrera López JL, Analuisa Jiménez EI, Velasco Acurio EF. Avances de la inteligencia artificial en salud. Dominio Las Cienc. 2019;5(3):603-13. DOI: https://doi.org/10.23857/dc.v5i3.955
- Bharati J, Jha V, Levin A. The Global Kidney Health Atlas: Burden and Opportunities to Improve Kidney Health Worldwide. Ann Nutr Metab. 2020;76 Suppl 1:25-30. DOI: https://doi.org/10.1159/000515329
- Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2020;395(10225):709-33. https://doi.org/10.1016/S0140-6736(20)30045-3 DOI: https://doi.org/10.1016/S0140-6736(19)32977-0
- Liao PH, Hsu PT, Chu W, Chu WC. Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan. Health Informatics J. 2015;21(2):137-48. https://doi.org/10.1177/1460458213509806 DOI: https://doi.org/10.1177/1460458213509806
- Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Published online March 29, 2021. https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.1136/bmj.n71
- Flechet M, Falini S, Bonetti C, et al. Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Crit Care Lond Engl. 2019;23(1):282. https://doi.org/10.1186/s13054-019-2563-x DOI: https://doi.org/10.1186/s13054-019-2563-x
- Martinez DA, Levin SR, Klein EY, et al. Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data. Ann Emerg Med. 2020;76(4):501-14. https://doi.org/10.1016/j.annemergmed.2020.05.026 DOI: https://doi.org/10.1016/j.annemergmed.2020.05.026
- Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care. 2021;27(6):560-72. https://doi.org/10.1097/MCC.0000000000000887 DOI: https://doi.org/10.1097/MCC.0000000000000887
- Chaudhuri S, Long A, Zhang H, et al. Artificial intelligence enabled applications in kidney disease. Semin Dial. 2021;34(1):5-16. https://doi.org/10.1111/sdi.12915 DOI: https://doi.org/10.1111/sdi.12915
- Wu X, Yuan X, Wang W, et al. Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension. Hypertens Dallas Tex 1979. 2020;75(5):1271-8. https://doi.org/10.1161/HYPERTENSIONAHA.119.13404 DOI: https://doi.org/10.1161/HYPERTENSIONAHA.119.13404
- Roth JA, Radevski G, Marzolini C, et al. Cohort-Derived Machine Learning Models for Individual Prediction of Chronic Kidney Disease in People Living With Human Immunodeficiency Virus: A Prospective Multicenter Cohort Study. J Infect Dis. 2021;224(7):1198-208. https://doi.org/10.1093/infdis/jiaa236 DOI: https://doi.org/10.1093/infdis/jiaa236
- Jacob AN, Khuder S, Malhotra N, et al. Neural network analysis to predict mortality in end-stage renal disease: application to United States Renal Data System. Nephron Clin Pract. 2010;116(2):c148-58. https://doi.org/10.1159/000315884 DOI: https://doi.org/10.1159/000315884
- Xi IL, Zhao Y, Wang R, et al. Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clin Cancer Res Off J Am Assoc Cancer Res. 2020;26(8):1944-52. https://doi.org/10.1158/1078-0432.CCR-19-0374 DOI: https://doi.org/10.1158/1078-0432.CCR-19-0374
- Byun SS, Heo TS, Choi JM, et al. Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma. Sci Rep. 2021;11(1):1242. https://doi.org/10.1038/s41598-020-80262-9 DOI: https://doi.org/10.1038/s41598-020-80262-9
- Purkayastha S, Zhao Y, Wu J, et al. Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm. Sci Rep. 2020;10(1):19503. https://doi.org/10.1038/s41598-020-76132-z DOI: https://doi.org/10.1038/s41598-020-76132-z
- Toda N, Hashimoto M, Arita Y, et al. Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database. Invest Radiol. 2022;57(5):327-33. https://doi.org/10.1097/RLI.0000000000000842 DOI: https://doi.org/10.1097/RLI.0000000000000842
- Raynaud M, Aubert O, Divard G, et al. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021;3(12):e795-e805. https://doi.org/10.1016/S2589-7500(21)00209-0 DOI: https://doi.org/10.1016/S2589-7500(21)00209-0
- Tangri N, Ansell D, Naimark D. Determining factors that predict technique survival on peritoneal dialysis: application of regression and artificial neural network methods. Nephron Clin Pract. 2011;118(2):c93-c100. https://doi.org/10.1159/000319988 DOI: https://doi.org/10.1159/000319988
- Hong L, Cheng X, Zheng D. Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease. Contrast Media Mol Imaging. 2021;2021:6423398. https://doi.org/10.1155/2021/6423398 DOI: https://doi.org/10.1155/2021/6423398
- Zhao C, Shi Q, Ma F, Yu J, Zhao A. Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease. Comput Math Methods Med. 2022;2022:6440138. https://doi.org/10.1155/2022/6440138 DOI: https://doi.org/10.1155/2022/6440138
- Barrera A, Gee C, Wood A, Gibson O, Bayley D, Geddes J. Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations. Evid Based Ment Health. 2020;23(1):34-8. https://doi.org/10.1136/ebmental-2019300136 DOI: https://doi.org/10.1136/ebmental-2019-300136
- An R, Chang GM, Fan YY, Ji LL, Wang XH, Hong S. Machine learning-based patient classification system for adult patients in intensive care units: A cross-sectional study. J Nurs Manag. 2021;29(6):1752-62. https://doi.org/10.1111/jonm.13284 DOI: https://doi.org/10.1111/jonm.13284
- Du Q, Liang D, Zhang L, Chen G, Li X. Evaluation of Functional Magnetic Resonance Imaging under Artificial Intelligence Algorithm on Plan-Do-Check-Action Home Nursing for Patients with Diabetic Nephropathy. Contrast Media Mol Imaging. 2022;2022:9882532. https://doi.org/10.1155/2022/9882532https://doi.org/10.1159/000515329 DOI: https://doi.org/10.1155/2022/9882532
- Brom H, Brooks Carthon JM, Ikeaba U, Chittams J. Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions. J Nurs Care Qual. 2020;35(1):27-33. https://doi.org/10.1097/NCQ.0000000000000412 DOI: https://doi.org/10.1097/NCQ.0000000000000412
- Bagnasco A, Siri A, Aleo G, Rocco G, Sasso L. Applying artificial neural networks to predict communication risks in the emergency department. J Adv Nurs. 2015;71(10):2293-304. https://doi.org/10.1111/jan.12691 DOI: https://doi.org/10.1111/jan.12691
- Azar AT, Wahba KM. Artificial neural network for prediction of equilibrated dialysis dose without intradialytic sample. Saudi J Kidney Dis Transplant Off Publ Saudi Cent Organ Transplant Saudi Arab. 2011;22(4):705-11.
- Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia. 2021;64(7):1504-15. https://doi.org/10.1007/s00125-021-05444-0 DOI: https://doi.org/10.1007/s00125-021-05444-0
- De Gonzalo-Calvo D, Martínez-Camblor P, Bär C, et al. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics. 2020;10(19):8665-76. https://doi.org/10.7150/thno.46123 DOI: https://doi.org/10.7150/thno.46123
- Barbieri C, Molina M, Ponce P, et al. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int. 2016;90(2):422-9. https://doi.org/10.1016/j.kint.2016.03.036 DOI: https://doi.org/10.1016/j.kint.2016.03.036
- Chen X, Huang X, Yin M. Implementation of Hospital-to-Home Model for Nutritional Nursing Management of Patients with Chronic Kidney Disease Using Artificial Intelligence Algorithm Combined with CT Internet. Contrast Media Mol Imaging. 2022;2022:1183988. https://doi.org/10.1155/2022/1183988 DOI: https://doi.org/10.1155/2022/1183988
- Yin P, Wang H. Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm. Comput Math Methods Med. 2022;2022:1786994. https://doi.org/10.1155/2022/1786994 DOI: https://doi.org/10.1155/2022/1786994
- Churpek MM, Carey KA, Edelson DP, et al. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury. JAMA Netw Open. 2020;3(8):e2012892. https://doi.org/10.1001/jamanetworkopen.2020.12892 DOI: https://doi.org/10.1001/jamanetworkopen.2020.12892
- Roblot V, Giret Y, Mezghani S, et al. Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma. Eur Radiol. 2022;32(7):4728-37. https://doi.org/10.1007/s00330-022-08579-9 DOI: https://doi.org/10.1007/s00330-022-08579-9
- Xiao J, Ding R, Xu X, et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J Transl Med. 2019;17(1):119. https://doi.org/10.1186/s12967-019-1860-0 DOI: https://doi.org/10.1186/s12967-019-1860-0
- Liu Y, Tang S. Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia. Comput Math Methods Med. 2022;2022:5823720. https://doi.org/10.1155/2022/5823720 DOI: https://doi.org/10.1155/2022/5823720
- Barbieri C, Mari F, Stopper A, et al. A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Comput Biol Med. 2015;61:56-61. http://doi.org/:10.1016/j.compbiomed.2015.03.019 DOI: https://doi.org/10.1016/j.compbiomed.2015.03.019
- Smith BP, Ward RA, Brier ME. Prediction of anticoagulation during hemodialysis by population kinetics and an artificial neural network. Artif Organs. 1998;22(9):731-9. http://doi.org/10.1046/j.1525-1594.1998.06101.x DOI: https://doi.org/10.1046/j.1525-1594.1998.06101.x
References
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40. https://doi.org/10.1016/j.metabol.2017.01.011 DOI: https://doi.org/10.1016/j.metabol.2017.01.011
Chen M, Decary M. Artificial intelligence in healthcare: An essential guide for health leaders. Healthc Manage Forum. 2020;33(1):10-8. https://doi.org/10.1177/0840470419873123 DOI: https://doi.org/10.1177/0840470419873123
Martínez García DN, Dalgo Flores VM, Herrera López JL, Analuisa Jiménez EI, Velasco Acurio EF. Avances de la inteligencia artificial en salud. Dominio Las Cienc. 2019;5(3):603-13. DOI: https://doi.org/10.23857/dc.v5i3.955
Bharati J, Jha V, Levin A. The Global Kidney Health Atlas: Burden and Opportunities to Improve Kidney Health Worldwide. Ann Nutr Metab. 2020;76 Suppl 1:25-30. DOI: https://doi.org/10.1159/000515329
Bikbov B, Purcell CA, Levey AS, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet. 2020;395(10225):709-33. https://doi.org/10.1016/S0140-6736(20)30045-3 DOI: https://doi.org/10.1016/S0140-6736(19)32977-0
Liao PH, Hsu PT, Chu W, Chu WC. Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan. Health Informatics J. 2015;21(2):137-48. https://doi.org/10.1177/1460458213509806 DOI: https://doi.org/10.1177/1460458213509806
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Published online March 29, 2021. https://doi.org/10.1136/bmj.n71 DOI: https://doi.org/10.1136/bmj.n71
Flechet M, Falini S, Bonetti C, et al. Machine learning versus physicians’ prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor. Crit Care Lond Engl. 2019;23(1):282. https://doi.org/10.1186/s13054-019-2563-x DOI: https://doi.org/10.1186/s13054-019-2563-x
Martinez DA, Levin SR, Klein EY, et al. Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data. Ann Emerg Med. 2020;76(4):501-14. https://doi.org/10.1016/j.annemergmed.2020.05.026 DOI: https://doi.org/10.1016/j.annemergmed.2020.05.026
Ozrazgat-Baslanti T, Loftus TJ, Ren Y, Ruppert MM, Bihorac A. Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury. Curr Opin Crit Care. 2021;27(6):560-72. https://doi.org/10.1097/MCC.0000000000000887 DOI: https://doi.org/10.1097/MCC.0000000000000887
Chaudhuri S, Long A, Zhang H, et al. Artificial intelligence enabled applications in kidney disease. Semin Dial. 2021;34(1):5-16. https://doi.org/10.1111/sdi.12915 DOI: https://doi.org/10.1111/sdi.12915
Wu X, Yuan X, Wang W, et al. Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension. Hypertens Dallas Tex 1979. 2020;75(5):1271-8. https://doi.org/10.1161/HYPERTENSIONAHA.119.13404 DOI: https://doi.org/10.1161/HYPERTENSIONAHA.119.13404
Roth JA, Radevski G, Marzolini C, et al. Cohort-Derived Machine Learning Models for Individual Prediction of Chronic Kidney Disease in People Living With Human Immunodeficiency Virus: A Prospective Multicenter Cohort Study. J Infect Dis. 2021;224(7):1198-208. https://doi.org/10.1093/infdis/jiaa236 DOI: https://doi.org/10.1093/infdis/jiaa236
Jacob AN, Khuder S, Malhotra N, et al. Neural network analysis to predict mortality in end-stage renal disease: application to United States Renal Data System. Nephron Clin Pract. 2010;116(2):c148-58. https://doi.org/10.1159/000315884 DOI: https://doi.org/10.1159/000315884
Xi IL, Zhao Y, Wang R, et al. Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging. Clin Cancer Res Off J Am Assoc Cancer Res. 2020;26(8):1944-52. https://doi.org/10.1158/1078-0432.CCR-19-0374 DOI: https://doi.org/10.1158/1078-0432.CCR-19-0374
Byun SS, Heo TS, Choi JM, et al. Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma. Sci Rep. 2021;11(1):1242. https://doi.org/10.1038/s41598-020-80262-9 DOI: https://doi.org/10.1038/s41598-020-80262-9
Purkayastha S, Zhao Y, Wu J, et al. Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm. Sci Rep. 2020;10(1):19503. https://doi.org/10.1038/s41598-020-76132-z DOI: https://doi.org/10.1038/s41598-020-76132-z
Toda N, Hashimoto M, Arita Y, et al. Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database. Invest Radiol. 2022;57(5):327-33. https://doi.org/10.1097/RLI.0000000000000842 DOI: https://doi.org/10.1097/RLI.0000000000000842
Raynaud M, Aubert O, Divard G, et al. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021;3(12):e795-e805. https://doi.org/10.1016/S2589-7500(21)00209-0 DOI: https://doi.org/10.1016/S2589-7500(21)00209-0
Tangri N, Ansell D, Naimark D. Determining factors that predict technique survival on peritoneal dialysis: application of regression and artificial neural network methods. Nephron Clin Pract. 2011;118(2):c93-c100. https://doi.org/10.1159/000319988 DOI: https://doi.org/10.1159/000319988
Hong L, Cheng X, Zheng D. Application of Artificial Intelligence in Emergency Nursing of Patients with Chronic Obstructive Pulmonary Disease. Contrast Media Mol Imaging. 2021;2021:6423398. https://doi.org/10.1155/2021/6423398 DOI: https://doi.org/10.1155/2021/6423398
Zhao C, Shi Q, Ma F, Yu J, Zhao A. Intelligent Algorithm-Based Ultrasound Image for Evaluating the Effect of Comprehensive Nursing Scheme on Patients with Diabetic Kidney Disease. Comput Math Methods Med. 2022;2022:6440138. https://doi.org/10.1155/2022/6440138 DOI: https://doi.org/10.1155/2022/6440138
Barrera A, Gee C, Wood A, Gibson O, Bayley D, Geddes J. Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations. Evid Based Ment Health. 2020;23(1):34-8. https://doi.org/10.1136/ebmental-2019300136 DOI: https://doi.org/10.1136/ebmental-2019-300136
An R, Chang GM, Fan YY, Ji LL, Wang XH, Hong S. Machine learning-based patient classification system for adult patients in intensive care units: A cross-sectional study. J Nurs Manag. 2021;29(6):1752-62. https://doi.org/10.1111/jonm.13284 DOI: https://doi.org/10.1111/jonm.13284
Du Q, Liang D, Zhang L, Chen G, Li X. Evaluation of Functional Magnetic Resonance Imaging under Artificial Intelligence Algorithm on Plan-Do-Check-Action Home Nursing for Patients with Diabetic Nephropathy. Contrast Media Mol Imaging. 2022;2022:9882532. https://doi.org/10.1155/2022/9882532https://doi.org/10.1159/000515329 DOI: https://doi.org/10.1155/2022/9882532
Brom H, Brooks Carthon JM, Ikeaba U, Chittams J. Leveraging Electronic Health Records and Machine Learning to Tailor Nursing Care for Patients at High Risk for Readmissions. J Nurs Care Qual. 2020;35(1):27-33. https://doi.org/10.1097/NCQ.0000000000000412 DOI: https://doi.org/10.1097/NCQ.0000000000000412
Bagnasco A, Siri A, Aleo G, Rocco G, Sasso L. Applying artificial neural networks to predict communication risks in the emergency department. J Adv Nurs. 2015;71(10):2293-304. https://doi.org/10.1111/jan.12691 DOI: https://doi.org/10.1111/jan.12691
Azar AT, Wahba KM. Artificial neural network for prediction of equilibrated dialysis dose without intradialytic sample. Saudi J Kidney Dis Transplant Off Publ Saudi Cent Organ Transplant Saudi Arab. 2011;22(4):705-11.
Chan L, Nadkarni GN, Fleming F, et al. Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease. Diabetologia. 2021;64(7):1504-15. https://doi.org/10.1007/s00125-021-05444-0 DOI: https://doi.org/10.1007/s00125-021-05444-0
De Gonzalo-Calvo D, Martínez-Camblor P, Bär C, et al. Improved cardiovascular risk prediction in patients with end-stage renal disease on hemodialysis using machine learning modeling and circulating microribonucleic acids. Theranostics. 2020;10(19):8665-76. https://doi.org/10.7150/thno.46123 DOI: https://doi.org/10.7150/thno.46123
Barbieri C, Molina M, Ponce P, et al. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int. 2016;90(2):422-9. https://doi.org/10.1016/j.kint.2016.03.036 DOI: https://doi.org/10.1016/j.kint.2016.03.036
Chen X, Huang X, Yin M. Implementation of Hospital-to-Home Model for Nutritional Nursing Management of Patients with Chronic Kidney Disease Using Artificial Intelligence Algorithm Combined with CT Internet. Contrast Media Mol Imaging. 2022;2022:1183988. https://doi.org/10.1155/2022/1183988 DOI: https://doi.org/10.1155/2022/1183988
Yin P, Wang H. Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm. Comput Math Methods Med. 2022;2022:1786994. https://doi.org/10.1155/2022/1786994 DOI: https://doi.org/10.1155/2022/1786994
Churpek MM, Carey KA, Edelson DP, et al. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury. JAMA Netw Open. 2020;3(8):e2012892. https://doi.org/10.1001/jamanetworkopen.2020.12892 DOI: https://doi.org/10.1001/jamanetworkopen.2020.12892
Roblot V, Giret Y, Mezghani S, et al. Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma. Eur Radiol. 2022;32(7):4728-37. https://doi.org/10.1007/s00330-022-08579-9 DOI: https://doi.org/10.1007/s00330-022-08579-9
Xiao J, Ding R, Xu X, et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J Transl Med. 2019;17(1):119. https://doi.org/10.1186/s12967-019-1860-0 DOI: https://doi.org/10.1186/s12967-019-1860-0
Liu Y, Tang S. Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia. Comput Math Methods Med. 2022;2022:5823720. https://doi.org/10.1155/2022/5823720 DOI: https://doi.org/10.1155/2022/5823720
Barbieri C, Mari F, Stopper A, et al. A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Comput Biol Med. 2015;61:56-61. http://doi.org/:10.1016/j.compbiomed.2015.03.019 DOI: https://doi.org/10.1016/j.compbiomed.2015.03.019
Smith BP, Ward RA, Brier ME. Prediction of anticoagulation during hemodialysis by population kinetics and an artificial neural network. Artif Organs. 1998;22(9):731-9. http://doi.org/10.1046/j.1525-1594.1998.06101.x DOI: https://doi.org/10.1046/j.1525-1594.1998.06101.x