Kidney Stone

Kidney stone identification is a crucial aspect of urological care, aiming to improve diagnosis speed and treatment efficacy by accurately classifying stone types. Current research heavily utilizes deep learning, employing architectures like ResNet and Inception, often enhanced with techniques such as multi-view fusion, attention mechanisms, and transfer learning to improve classification accuracy and robustness, particularly when dealing with limited data or variations in image acquisition conditions. These advancements are driven by the need for faster, more reliable in-vivo diagnosis during ureteroscopy, potentially reducing the reliance on time-consuming and expensive ex-vivo analysis. Furthermore, efforts are underway to develop more interpretable models, providing clinicians with understandable reasoning behind classification decisions.