Identifying urethral strictures using machine learning: a proof-of-concept evaluation of convolutional neural network model.
World J Urol
; 40(12): 3107-3111, 2022 Dec.
Article
em En
| MEDLINE
| ID: mdl-36350384
ABSTRACT
INTRODUCTION:
To evaluate urethral strictures and to determine appropriate surgical reconstructive options, retrograde urethrograms (RUG) are used. Herein, we develop a convolutional neural network (CNN)-based machine learning algorithm to characterize RUG images between those with urethral strictures and those without urethral strictures.METHODS:
Following approval from institutional REB from participating institutions (The Hospital for Sick Children [Toronto, Canada], St. Luke's Medical Centre [Quezon City, Philippines], East Virginia Medical School [Norfolk, United States of America]), retrograde urethrogram images were collected and anonymized. Additional RUG images were downloaded online using web scraping method through Selenium and Python 3.8.2. A CNN with three convolutional layers and three pooling layers were built (Fig. 1). Data augmentation was applied with zoom, contrast, horizontal flip, and translation. The data were split into 90% training and 10% testing set. The model was trained with one hundred epochs.RESULTS:
A total of 242 RUG images were identified. 196 were identified as strictures and 46 as normal. Following training, our model achieved accuracy of up to 92.2% with its training data set in characterizing RUG images to stricture and normal images. The validation accuracy using our testing set images showed that it was able to characterize 88.5% of the images correctly.CONCLUSION:
It is feasible to use a machine learning algorithm to accurately differentiate between a stricture and normal RUG. Further development of the model with additional RUGs may allow characterization of stricture location and length to suggest optimal operative approach for repair.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Estreitamento Uretral
Limite:
Child
/
Humans
Idioma:
En
Revista:
World J Urol
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Canadá