Miscellaneous

The following models are related miscellenaous topics in pediatric urology with < 3 models published per topic. They pertain to hypospadias, voiding dysfunction, kidney transplant, among others.

Nedbal et al. (2024)

Stone free status after ureteroscopy Article DOI https://dx.doi.org/10.1089/end.2024.0120 Objective To predict postoperative ureteroscopy outcomes in children (stone-free status and complications) from preoperative characteristics AI Approach LR, SVM, DT, RF, Extra Trees (boosted), Naive Bayes, KNN, Bagging; Ensemble (Bagging + Extra Trees + LDA), Multitask ANN Data Source(s) Single institutional series (146 patients) Model Input Age,…

Abudurexiti et al. (2025)

SIRS after PCNL Article DOI https://doi.org/10.2147/JIR.S518631 Objective To predict systemic inflammatory response syndrome (SIRS) in pediatric patients undergoing percutaneous nephrolithotripsy AI Approach Random forest, XGBoost, Logistic regression, K-nearest neighbours, Light gradient boosting machine (LightGBM), decision tree, SVM, naive bayes (NB) Data Source(s) Single institutional series (463 patients) Model Input Operation time, stone burden, staghorn stones,…

Lin et al. (2023)

Differential normal and abnormal kidneys, Imaging. Article DOI https://doi.org/10.1016/j.crad.2023.04.015 Objective To differentiate normal from abnormal/scarred kidneys using SPECT in pediatric patients on DMSA scan AI Approach CNN Data Source(s) Single institutional series (301 patients) Model Input 3D SPECT, 2D Maximum Intensity Projections, 2.5D Maximum Intensity Projections Model Outcome Abnormal kidneys Model Metrics AUC: 0.91 (3D),…

Eksi et al. (2022)

Orchiectomy in torsion, Testicular torsion. Article DOI https://doi.org/10.1007/s00383-022-05185-0 Objective To predict the likelihood of orchiectomy rather than orchiopexy in testicular torsion using preoperative features AI Approach Random forrest Data Source(s) Single institutional series (256 children in the development set, 44 children in the testing set) Model Input 26 parameters from clinical findings and US, including…

Celik et al. (2020)

Renal scarring in lower urinary tract dysfunction, Misc. Article DOI https://doi.org/10.28982/josam.691768 Objective To predict renal scarring in children with LUTD AI Approach 7 models were tested: ANN and XGB were superior Data Source(s) Single institutional series (75 patients) Model Input Clinical Features (episodes of symptomatic UTI, presence of VUR, bladder trabeculation, bladder wall thickness, catherization),…

Zheng et al. (2019)

CAKUT vs. normal kidneys, Miscelleneous. Article DOI https://doi.org/10.1016/j.jpurol.2018.10.020 Objective To classify kidneys of normal children and those with CAKUT AI Approach SVM and CNN with transfer learning Data Source(s) Institutional series (100 children) Model Input Features from segmented kidneys by transfer learning and conventional imaging Model Outcome CAKUT Model Metrics AUROC = 0.81-0.92 Model Usability…

Tokar et al. (2012)

Enuresis, Miscelleneous Article DOI https://doi.org/10.1055/s-0040-1715655 Objective To predict enuresis in children AI Approach Logistic regression, Trees, Bayes, SVM, Deep Learning Data Source(s) Administrative dataset (8071 children) Model Input 14 variables (clinical factors, urinary habits, family history, lower urinary tract symptoms) Model Outcome Enuresis Model Metrics AUROC = 0.81, accuracy = 81% Model Usability NA AI…