VUR + UTI

The following models are related to vesicoureteral reflux (VUR), urinary tract infections (UTI) and related models.

Alqaraleh et al. (2025)

VUR grade prediction Article DOI https://doi.org/10.56294/dm2025460 Objective To determine VUR grade using images from VCUGs AI Approach Random forrest Data Source(s) Online image scraping (113 VCUG images) Model Input VCUG images ranging from Grade 1 to Grade 5 Model Outcome Grades 1 to 5 Model Metrics AUC 1.00, accuracy 1.00, sensitivity 1.00, specificity 1.00 Model…

Chen et al. (2025)

Grading VUR with deep learning Article DOI https://doi.org/10.1007/s10278-025-01438-1 Objective To grade VUR from VCUG images AI Approach CNN, independently analyzing the left and right urinary tracts Data Source(s) Single institutional series (1529 patients) Model Input VCUG images (768 x 768 pixels) Model Outcome VUR grade 0 vs 1-2 vs 3-5 Model Metrics AUC of 0.82;…

Tafazoli et al. (2025)

Antibiotic prophylaxis in VUR Article DOI https://doi.org/10.1038/s41598-025-92847-3 Objective To predict which patients with vesicoureteral reflux are most likely to benefit from continuous antibiotic prophylaxis AI Approach Logistic regression, random forest, SVM, gradient boosting, CNN Data Source(s) Two-centre institutional series (225 patients) Model Input Gender, age at diagnosis, uni- or bilaterality of VUR, DMSA differential renal…

Wang et al. (2024)

Dilating VUR from ultrasound, VUR + UTI. Article DOI https://doi.org/10.1016/j.jpurol.2023.11.003 Objective To reliably predict dilating VUR from early postnatal US in patients with hydronephrosis AI Approach Optimal Classification Tree Data Source(s) Single institutional series (280 patients, 530 renal units) Model Input Patient demographics (age at US, sex, kidney laterality) and UTD features (primary antero-posterior diameter,…

Li et al. (2024)

VUR grading, VUR + UTI. Article DOI https://doi.org/10.1016/j.eclinm.2024.102466 Objective To develop and validate a deep-VCUG with ensemble learning for automatic VUR grading from VCUG images AI Approach CNN, Ensemble learning Data Source(s) Multi-institutional series (1948 images, from 5 institutions) Model Input 512 x 512 pixels VCUG images Model Outcome Unilateral and Bilateral VUR, VUR Grade…

Knudson et al. (2007)

Resolution in VUR, VUR + UTI. Article DOI https://doi.org/10.1016/j.juro.2007.03.161 Objective To predict the chance of early VUR resolution. AI Approach Linear Support Vector Machines Data Source(s) Single institutional series (205 patients) Model Input Age, gender, presenting symptoms, reflux grade, laterality, whether reflex occurred during filling or voiding, initial bladder volume at onset, presence of complete…

Ganapathy et al. (2023)

Renal scarring in VUR, VUR + UTI. Article DOI https://doi.org/10.1007/s11845-023-03275-z Objective To predict renal scarring in pediatric population with VUR AI Approach Logistic Regression, Discriminant Analysis, Bayesian Logistic Regression, Naïve Bayes, Decision Tree Data Source(s) Single institutional series (94 patients) Model Input Kidney injury molecule-1 (KIM-1), Neutrophil gelatinase–associated lipocalin (NGAL), Urinary creatinine, Ratios of NGAL…

Dubrov et al. (2021)

Endoscopic injection in VUR, VUR + UTI. Article DOI https://doi.org/10.21886/2308-6424-2021-9-2-45-55 Objective To predict the outcomes of a single endoscopic injection of DxHA for correction of VUR AI Approach Multilayer ANN (multilayer perceptron) with two hidden layers and a sigmoid function of neuronal activation Data Source(s) Multi-institutional series (582 children, 783 ureteric units operated on) Model…

Arlen et al. (2016)

To predict breakthrough UTI, VUR + UTI. Article DOI https://doi.org/10.1016/j.jpurol.2016.03.005 Objective To predict the probability of breakthrough fUTI in children with primary VUR. AI Approach 2-hidden node neural network Data Source(s) Single institutional series (384 patients) Model Input Age, gender, laterality, percentage PBC at VUR onset, VUR grade right/left, VUR onset right/left (filling or voiding),…

Kabir et al. (2023)

VUR grade, Vesicoureteral Reflux and Urinary Tract Infection Article DOI https://doi.org/10.1016/j.jpurol.2023.10.030 Objective To predict VUR grade from VCUG AI Approach Multiple classification algorithms (MLP, Extra Trees, Random Forrest, Gradient Boosted Trees, SVM) Data Source(s) Online image scraping (113 VCUG images) Model Input VCUG Image, Nine input features Model Outcome Binary VUR grade (low [I-III] vs.…

Lee et al. (2022)

Vesicoureteral Reflux and Urinary Tract Infection Article DOI https://doi.org/10.3390/diagnostics12020424 Objective To predict the recurrence of UTI after 99mTc-DMSA renal scan AI Approach CNN Data Source(s) Institutional series (180 patients) Model Input Pre-processed 99mTc-DMSA images Model Outcome Recurrent UTI Model Metrics Accuracy = 91% Model Usability NA AI = Artificial intelligence, AUROC = Area under the…

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