Prediction of the composition of urinary stones using deep learning.

Ui Seok Kim Hyo Sang Kwon Wonjong Yang Wonchul Lee Chang Il Choi Jong Keun Kim Seong Ho Lee Dohyoung Rim Jun Hyun Han

Published in: Investigative and clinical urology (2022)

This study showed the feasibility of deep learning for the diagnostic ability to assess urinary stone composition from images. It can be an alternative tool for conventional stone analysis and provide decision support to urologists, improving the effectiveness of diagnosis and treatment.

Keyphrases

deep learning
convolutional neural network
artificial intelligence
machine learning
randomized controlled trial
systematic review
editorial comment