In JMIR research protocols ; h5-index 26.0
BACKGROUND : Hypospadias remains the most prevalent congenital abnormality in boys worldwide. However, the limited infrastructure and number of pediatric urologists capable of diagnosing and managing the condition hinder the management of hypospadias in Indonesia. The use of artificial intelligence and image recognition is thought to be beneficial in improving the management of hypospadias cases in Indonesia.
OBJECTIVE : We aim to develop and validate a digital pattern recognition system and a mobile app based on an artificial neural network to determine various parameters of hypospadias.
METHODS : Hypospadias and normal penis images from an age-matched database will be used to train the artificial neural network. Images of 3 aspects of the penis (ventral, dorsal, and lateral aspects, which include the glans, shaft, and scrotum) will be taken from each participant. The images will be labeled with the following hypospadias parameters: hypospadias status, meatal location, meatal shape, the quality of the urethral plate, glans diameter, and glans shape. The data will be uploaded to train the image recognition model. Intrarater and interrater analyses will be performed, using the test images provided to the algorithm.
RESULTS : Our study is at the protocol development stage. A preliminary study regarding the system's development and feasibility will start in December 2022. The results of our study are expected to be available by the end of 2023.
CONCLUSIONS : A digital pattern recognition system using an artificial neural network will be developed and designed to improve the diagnosis and management of patients with hypospadias, especially those residing in regions with limited infrastructure and health personnel.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) : PRR1-10.2196/42853.
Wahyudi Irfan, Utomo Chandra Prasetyo, Djauzi Samsuridjal, Fathurahman Muhamad, Situmorang Gerhard Reinaldi, Rodjani Arry, Yonathan Kevin, Santoso Budi
2022-Nov-25
artificial intelligence, digital recognition, hypospadias, machine learning
