Ureteral stents are the most commonly used postoperative implantable materials in urology. The indwelling ureteral stents often lead to the formation of encrustation, with an incidence of 10%, which will lead to complications such as obstruction, lower urinary tract symptom (LUTS）, and difficulty in extubation. At present, there is no accurate and sensitive diagnostic criteria for ureteral stents encrustation. Previously, we developed the artificial intelligence-based ureteral stents encrustation identification software - GREE-AI Medical Software (GAIMS) with the collaboration of GREE ELECTRIC APPLIANCES,INCORPORATED.OF ZHUHAI(a city of Guangdong, China). In order to further evaluate the diagnostic sensitivity and accuracy of the software in practical clinical applications, and to verify its predictive ability for ureteral stents encrustation, the following single-center, prospective, double-blind, diagnostic cohort study is planned. This study will use the gold standard as a reference to evaluate the sensitivity and specificity of GAIMS for detecting ureteral stents encrustation. At the same time, it will be compared with non-enhanced contrast Computed Tomography examinations interpreted by clinical radiologists to evaluate the pros and cons of GAIMS in detecting ureteral stents encrustation.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Number of participants with ureteral stent encrustation longer than 4mm measured by a ruler
Timeframe: When the patient returns to the hospital to remove the ureteral stent, it may be 2 weeks to 1 year after the operation.