Furth SL, Cole SR, Moxey-Mims M, Kaskel F, Mak R, Schwartz G, Wong C, Muñoz A, Warady BA (2006) Design and methods of the Chronic Kidney Disease in Children (CKiD) prospective cohort study. Clin J Am Soc Nephrol 1:1006–1015. https://doi.org/10.2215/CJN.01941205

Article  PubMed  PubMed Central  Google Scholar 

Atkinson MA, Ng DK, Warady BA, Furth SL, Flynn JT (2021) The CKiD study: overview and summary of findings related to kidney disease progression. Pediatr Nephrol 36:527–538. https://doi.org/10.1007/s00467-019-04458-6

Article  PubMed  Google Scholar 

Smith JM, Stablein DM, Munoz R, Hebert D, McDonald RA (2007) Contributions of the transplant registry: the 2006 annual report of the north American pediatric renal trials and collaborative studies (NAPRTCS). Pediatr Transplant 11:366–373. https://doi.org/10.1111/j.1399-3046.2007.00704.x

Article  PubMed  Google Scholar 

Chua A, Cramer C, Moudgil A, Martz K, Smith J, Blydt-Hansen T, Neu A, Dharnidharka VR, NAPRTCS investigators (2019) Kidney transplant practice patterns and outcome benchmarks over 30 years: the 2018 report of the NAPRTCS. Pediatr Transplant 23:e13597. https://doi.org/10.1111/petr.13597

Article  PubMed  Google Scholar 

Bassanese G, Wlodkowski T, Servais A, Heidet L, Roccatello D, Emma F, Levtchenko E, Ariceta G, Bacchetta J, Capasso G, Jankauskiene A, Miglinas M, Ferraro PM, Montini G, Oh J, Decramer S, Levart TK, Wetzels J, Cornelissen E, Devuyst O, Zurowska A, Pape L, Buescher A, Haffner D, Marcun Varda N, Ghiggeri GM, Remuzzi G, Konrad M, Longo G, Bockenhauer D, Awan A, Andersone I, Groothoff JW, Schaefer F (2021) The European Rare Kidney Disease Registry (ERKReg): objectives, design and initial results. Orphanet J Rare Dis 16:251. https://doi.org/10.1186/s13023-021-01872-8

Article  PubMed  PubMed Central  Google Scholar 

Hernán MA, Robins JM (2016) Using big data to emulate a target trial when a randomized trial is not available: table 1. Am J Epidemiol 183:758–764. https://doi.org/10.1093/aje/kwv254

Article  PubMed  PubMed Central  Google Scholar 

Zoccali C, Tripepi G (2025) Clinical trial emulation in nephrology. J Nephrol 38:11–23. https://doi.org/10.1007/s40620-024-02158-5

Article  PubMed  Google Scholar 

Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix A-L, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P (2024) TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385:e078378. https://doi.org/10.1136/bmj-2023-078378

Article  PubMed  PubMed Central  Google Scholar 

Liu X, Cruz Rivera S, Moher D, Calvert MJ (2020) Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26:1364–1374. https://doi.org/10.1038/s41591-020-1034-x

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cruz Rivera S, Liu X, Chan A-W, Denniston AK, Calvert MJ (2020) Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26:1351–1363. https://doi.org/10.1038/s41591-020-1037-7

Article  CAS  PubMed  PubMed Central  Google Scholar 

Nada A, Ahmed Y, Hu J, Weidemann D, Gorman GH, Lecea EG, Sandokji IA, Cha S, Shin S, Bani-Hani S, Mannemuddhu SS, Ruebner RL, Kakajiwala A, Raina R, George R, Elchaki R, Moritz ML; The American Society of Pediatric Nephrology Quality Improvement and Artificial Intelligence (ASPN QI/AI) Interest Group (2025) AI-powered insights in pediatric nephrology: current applications and future opportunities. Pediatr Nephrol. https://doi.org/10.1007/s00467-025-06911-1

Lemley KV (2025) A brief review of some artificial intelligence methods in nephrology. Pediatr Nephrol. https://doi.org/10.1007/s00467-025-06995-9

Article  PubMed  Google Scholar 

Yin S, Peng Q, Li H, Zhang Z, You X, Fischer K, Furth SL, Fan Y, Tasian GE (2020) Multi-instance deep learning of ultrasound imaging data for pattern classification of congenital abnormalities of the kidney and urinary tract in children. Urology 142:183–189. https://doi.org/10.1016/j.urology.2020.05.019

Article  PubMed  PubMed Central  Google Scholar 

Weaver JK, Milford K, Rickard M, Logan J, Erdman L, Viteri B, D’Souza N, Cucchiara A, Skreta M, Keefe D, Shah S, Selman A, Fischer K, Weiss DA, Long CJ, Lorenzo A, Fan Y, Tasian GE (2023) Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves. Pediatr Nephrol 38:839–846. https://doi.org/10.1007/s00467-022-05677-0

Article  PubMed  Google Scholar 

Ng DK, Matheson MB, Schwartz GJ, Wang FM, Mendley SR, Furth SL, Warady BA (2023) Development of an adaptive clinical web-based prediction tool for kidney replacement therapy in children with chronic kidney disease. Kidney Int 104:985–994. https://doi.org/10.1016/j.kint.2023.06.020

Article  CAS  PubMed  PubMed Central  Google Scholar 

Moon S, Noh J, Kang HG, Lee KH, Shin JI, Song JY, Baek HS, Cho MH, Jung J, Lee JH, Cho H, Han KH, Yang EM, Kim SH, Kim JH, Park E, Ahn YH (2025) Development of a prediction tool for kidney function decline in children with chronic kidney disease. Kidney Res Clin Pract. https://doi.org/10.23876/j.krcp.25.004

Zheng Q, Tastan G, Fan Y (2018) Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data. Proc IEEE Int Symp Biomed Imaging 2018:1487–1490

PubMed  PubMed Central  Google Scholar 

Zheng Q, Furth SL, Tasian GE, Fan Y (2019) Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol 15:75.e1-75.e7. https://doi.org/10.1016/j.jpurol.2018.10.020

Article  CAS  PubMed  Google Scholar 

Zhang H, Wang L-C, Chaudhuri S, Pickering A, Usvyat L, Larkin J, Waguespack P, Kuang Z, Kooman JP, Maddux FW, Kotanko P (2023) Real-time prediction of intradialytic hypotension using machine learning and cloud computing infrastructure. Nephrol Dial Transplant 38:1761–1769. https://doi.org/10.1093/ndt/gfad070

Article  PubMed  PubMed Central  Google Scholar 

Lee H, Yun D, Yoo J, Yoo K, Kim YC, Kim DK, Oh K-H, Joo KW, Kim YS, Kwak N, Han SS (2021) Deep learning model for real-time prediction of intradialytic hypotension. Clin J Am Soc Nephrol 16:396–406. https://doi.org/10.2215/CJN.09280620

Article  PubMed  PubMed Central  Google Scholar 

Hermsen M, de Bel T, den Boer M, Steenbergen EJ, Kers J, Florquin S, Roelofs JJTH, Stegall MD, Alexander MP, Smith BH, Smeets B, Hilbrands LB, van der Laak JAWM (2019) Deep learning-based histopathologic assessment of kidney tissue. J Am Soc Nephrol 30:1968–1979. https://doi.org/10.1681/ASN.2019020144

Article  PubMed  PubMed Central  Google Scholar 

Barbieri C, Cattinelli I, Neri L, Mari F, Ramos R, Brancaccio D, Canaud B, Stuard S (2019) Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: proof of concept and first clinical assessment. Kidney Dis (Basel) 5:28–33. https://doi.org/10.1159/000493479

Article  PubMed  Google Scholar 

Smail LC, Dhindsa K, Braga LH, Becker S, Sonnadara RR (2020) Using deep learning algorithms to grade hydronephrosis severity: toward a clinical adjunct. Front Pediatr 8:1. https://doi.org/10.3389/fped.2020.00001

Article  PubMed  PubMed Central  Google Scholar 

Song SH, Han JH, Kim KS, Cho YA, Youn HJ, Kim YI, Kweon J (2022) Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio. Investig Clin Urol 63:455–463. https://doi.org/10.4111/icu.20220085

Article  PubMed  PubMed Central  Google Scholar 

Sloan M, Li H, Lescay HA, Judge C, Lan L, Hajiyev P, Giger ML, Gundeti MS (2023) Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound. Investig Clin Urol 64:588–596. https://doi.org/10.4111/icu.20230170

Article  PubMed  PubMed Central  Google Scholar 

Erdman L, Rickard M, Drysdale E, Skreta M, Hua SB, Sheth K, Alvarez D, Velaer KN, Chua ME, Dos Santos J, Keefe D, Rosenblum ND, Bonnett MA, Weaver J, Xiang A, Fan Y, Viteri B, Cooper CS, Tasian GE, Lorenzo AJ, Goldenberg A (2024) The hydronephrosis severity index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone. Sci Rep 14:22748. https://doi.org/10.1038/s41598-024-72271-9

Article  CAS  PubMed  PubMed Central  Google Scholar 

Khondker A, Hua SBZ, Kwong JCC, Sheth K, Alvarez D, Velaer KN, Weaver J, Xiang A, Tasian GE, Lorenzo AJ, Goldenberg A, Rickard M, Erdman L (2025) Longitudinal image-based prediction of surgical intervention in infants with hydronephrosis using deep learning: is a single ultrasound enough? PLoS Digit Health 4:e0000939. https://doi.org/10.1371/journal.pdig.0000939

Article  PubMed  PubMed Central  Google Scholar