- 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 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 assess the efficacy and safety of neurostimulation for non-neurogenic overactive bladder in children, we conducted a meta-analysis of randomized controlled trials (RCTs).
- 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.