Identifying urethral strictures using machine learning: a proof-of-concept evaluation of convolutional neural network model.

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.

An Unexpected Turn: An Unusual Case of a Metastatic Ovarian Carcinoma Arising from a Colorectal Malignancy.

Krukenberg tumors are very rare. Its origin is difficult to define especially if its gross features mimic a primary ovarian cancer. We present a case of a 24-year-old Filipino female patient with metastatic mucinous ovarian adenocarcinoma of colonic origin that mimicked primary ovarian cancer and genitourinary tuberculosis. Surgery was done and histopathology revealed that the cancer was a metastatic mucinous adenocarcinoma of colonic origin. This case highlights the importance of differentiating between benign and malignant ovarian lesions as well as distinction between primary and metastatic ovarian neoplasms. Radiological imaging has an evolving role in diagnosis of different cancers, which may be improved through better clinical correlation and developing meaningful differential diagnosis while advancing to a more strategized algorithm in the diagnostic approach.