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.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).
