Pediatric Urology
Digital Pattern Recognition for the Identification and Classification of Hypospadias Using Artificial Intelligence vs Experienced Pediatric Urologist
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.Female Authorship Publishing Trends and Forecasting in Pediatric Urology: Are We Closer to Gender Equality?
To review the literature of 5 pediatric urology topics and conduct gender based and forecasting analyses of first and corresponding authors.Testis-sparing Surgery for Pediatric Leydig Cell Tumors: Evidence of Favorable Outcomes Irrespective of Surgical Margins
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).Over-reliance on P Values in Urology: Fragility of Findings in the Hydronephrosis Literature Calls for Systematic Reporting of Robustness Indicators
To review the robustness of hydronephrosis literature with the application of fragility index (FI) and fragility quotient (FQ) calculations.Predictive Analytics and Modeling Employing Machine Learning Technology: The Next Step in Data Sharing, Analysis, and Individualized Counseling Explored With a Large, Prospective Prenatal Hydronephrosis Database
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).
