Kidney Stone Type

Identifying kidney stone types is crucial for effective treatment and recurrence prevention, currently relying on time-consuming and expert-dependent methods. Research focuses on developing automated in-vivo identification systems using deep learning (DL) models, including architectures like ResNet and Inception, and exploring metric learning approaches to handle data scarcity for rarer stone types. These efforts aim to improve diagnostic speed and accuracy during ureteroscopy, potentially leading to faster treatment decisions and reduced infection risks. Furthermore, research emphasizes the importance of model interpretability to build trust and facilitate clinical adoption.