- To improve hypospadias classification system, we hereby, show the use of machine learning/image recognition to increase objectivity of hypospadias recognition and classification. Hypospadias anatomical variables such as meatal location, quality of urethral plate, glans size, and ventral curvature have been identified as predictors for postoperative outcomes but there is still significant subjectivity between evaluators.
- To review the literature of 5 pediatric urology topics and conduct gender based and forecasting analyses of first and corresponding authors.
- To review our single institution experience, exploring the role of testis-sparing surgical resection in a cohort of children with Testicular Leydig cell tumors (LCTs).
- To review the robustness of hydronephrosis literature with the application of fragility index (FI) and fragility quotient (FQ) calculations.
- To explore the potential value of utilizing a commercially available cloud-based machine learning platform to predict surgical intervention in infants with prenatal hydronephrosis (HN).
- To explore the value of renal parenchyma-to-hydronephrosis area ratio (PHAR) in detecting trends of hydronephrosis (HN) improvement or worsening and response to surgical intervention.
- To explore the potential value of an objective assessment, renal parenchyma to hydronephrosis area ratio (PHAR), as an early predictor of surgery.
- To compare pyeloplasty outcomes in children with and without “supra-normal” differential renal function (SNDRF) defined as >55% differential renal function (DRF) in children with ureteropelvic junction obstruction.