Real-time Monitoring of Urinary Stone Status During Shockwave Lithotripsy

To develop a standardized, real-time feedback system for monitoring urinary stone fragmentation during shockwave lithotripsy (SWL), thereby optimizing treatment efficacy and minimizing patient risk.

A 2-pronged approach was implemented to quantify stone fragmentation in C-arm X-ray images. First, the initial pre-treatment stone image was compared to subsequent images to measure stone area loss. Second, a Convolutional Neural Network was trained to estimate the probability that an image contains a urinary stone. These 2 criteria were integrated to create a real-time signaling system capable of evaluating shockwave efficacy during SWL.

The system was developed using data from 522 shockwave treatments encompassing 4057 C-arm X-ray images. The combined area-loss metric and Convolutional Neural Network output enabled consistent real-time assessment of stone fragmentation, providing actionable feedback to guide SWL in diverse clinical contexts.

The proposed system offers a novel and reliable method for monitoring urinary stone fragmentation during SWL. By helping to balance treatment efficacy with patient safety, it holds significant promise for semi-automated SWL platforms, particularly in resource-limited or remote environments such as arid regions and extended space missions.