Surgery in ureteropelvic junction obstruction, Hydronephrosis.
| Article DOI | https://doi.org/10.5489/cuaj.10043 |
| Objective | To predict management (observation vs. surgery) for UPJO |
| AI Approach | ANN |
| Data Source(s) | Single institutional series (53 patients) |
| Model Input | Age, sex, renal pelvic diameter, laterality, split renal function on radionuclide scan, presence of renal scar on DMSA scan, urine culture results, presence of symptomatic urinary infections |
| Model Outcome | Need for surgery |
| Model Metrics | Sensitivity: 92% (training), 75% (test) Specificity: 77% (training) |
| Model Usability | NA |
AI = Artificial intelligence, UPJO = Ureteropelvic junction obstruction, SFU = Society for Fetal Urology, ANN = Artificial Neural Network
AIPEDURO 
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