Digital pattern recognition for the identification and classification of hypospadias using artificial intelligence vs experienced pediatric urologist
Objective: 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 c...
- Autores:
-
Fernandez, Nicolas
Lorenzo, Armando J.
Rickard, Mandy
Chua, Michael
Pippi-Salle, Joao L.
Perez Niño, Jaime
Braga, Luis H.
Matava, Clyde
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Pontificia Universidad Javeriana
- Repositorio:
- Repositorio Universidad Javeriana
- Idioma:
- eng
- OAI Identifier:
- oai:repository.javeriana.edu.co:10554/60051
- Acceso en línea:
- https://www.goldjournal.net/article/S0090-4295(20)31129-8/fulltext
http://hdl.handle.net/10554/60051
https://doi.org/10.1016/j.urology.2020.09.019
- Palabra clave:
- Article
Artificial Intelligence
Automated Pattern Recognition
Child Urology
Clinical Outcome
Clinician
Data Base
Diagnostic Accuracy
Disease Classification
Functional Assessment
Functional Disease
Human
Hypospadias
Image Analysis
Learning Al
- Rights
- License
- Atribución-NoComercial 4.0 Internacional