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.
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.
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.
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.
World journal of urology. 2022 Nov 09 [Epub ahead of print]
Jin Kyu Kim, Kurt McCammon, Catherine Robey, Marvin Castillo, Odina Gomez, Patricia Jarmin L Pua, Francis Pile, Manuel See, Mandy Rickard, Armando J Lorenzo, Michael E Chua
Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada. ., Department of Urology, Eastern Virginia Medical School, Norfolk, VA, USA., Institute of Urology, St. Luke's Medical Centre, Quezon City, Philippines., Section of Pediatric Imaging, Institute of Radiology, St. Luke's Medical Centre, Quezon City, Philippines., Division of Urology, Hospital for Sick Children, 555 University Ave, Toronto, ON, M5G 1X8, Canada., Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
PubMed http://www.ncbi.nlm.nih.gov/pubmed/36350384