# Toward Supportive Decision-Making for Ureteral Stent Removal: Development of a Morphology-Based X-Ray Analysis

## TL;DR

This paper develops a quantitative X-ray analysis method to support objective decisions for removing ureteral stents, improving precision in urological care.

## Contribution

A semi-automated image-processing algorithm is introduced to evaluate stent morphology, enabling reproducible and objective postoperative assessments.

## Key findings

- The slope-based method detected significant ureteral straightening (p < 0.05), aligning with clinical observations.
- The vector-angle method showed no significant temporal changes (p = 0.844).
- The algorithm provides reliable quantitative insights into postoperative morphological changes.

## Abstract

Purpose: Timely removal of ureteral stents is critical to prevent complications such as infection, discomfort and stent encrustation or fragmentation, as well as stone formation associated with neglected stents. Current decisions, however, rely heavily on subjective interpretation of postoperative imaging. This study introduces a semi-automated image-processing algorithm that quantitatively evaluates stent morphology, aiming to support objective and reproducible decision-making in minimally invasive urological care. Methods: Two computational approaches were developed to analyze morphological changes in ureteral stents following surgery. The first method employed a vector-based analysis, using the FitLine function to derive unit vectors for each stent segment and calculating inter-vector angles. The second method applied a slope-based analysis, computing gradients between coordinate points to evaluate global straightening of the ureter over time. Results: The vector-angle method did not demonstrate significant temporal changes (p = 0.844). In contrast, the slope-based method identified significant ureteral straightening (p < 0.05), consistent with clinical observations. These results confirm that slope-based quantitative analysis provides reliable insight into postoperative morphological changes. Conclusions: This study presents an algorithm-based and reproducible imaging analysis method that enhances objectivity in postoperative assessment of ureteral stents. By aligning quantitative image processing with clinical decision support, the approach contributes to precision medicine and addresses the absence of standardized criteria for stent removal.