Hydronephrosis
| Article DOI | https://doi.org/10.1016/j.urology.2018.05.041 |
| Objective | To predict obstructive hydronephrosis requiring surgery in children with prenatal hydronephrosis |
| AI Approach | Boosted decision tree, neural network |
| Data Source(s) | Institutional series (557 patients) |
| Model Input | Age, gender, affected side, SFU grade, renogram findings, ureteral dilatation, anteroposterior diameter |
| Model Outcome | Requiring surgery |
| Model Metrics | AUROC = 0.90, accuracy = 87% |
| Model Usability | NA |
AIPEDURO 
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