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 Usability | Web-scraped dataset is provided. |
AI = Artificial intelligence, VUR = Vesicoureteral reflux, AUC = Area under the receiver operator characteristic
AIPEDURO 
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