An Artificial Intelligence-Based System for Identifying Ureteral Stricture Regions

Purpose: This paper proposes a technical system designed to accurately identify the stenotic site in patients with ureteral stricture and guide the surgeon. The objective of this technical solution is to improve surgical efficiency.Methods: The proposed system applies the YOLOv5 algorithm, an artificial intelligence technology, to analyze real-time input images, detect the location of stenosis with high precision, and provide clinical support. The YOLOv5 algorithm was selected to enable rapid and accurate detection of the stenotic area.Results: The system demonstrated high recognition accuracy, yielding an average final sensitivity of 0.95. Because sensitivity reflects the probability of a true positive result, this finding confirms that the proposed method offered precise guidance for identifying the stenotic site.Conclusions: The proposed method, which utilizes the YOLOv5 algorithm, can support surgery for patients with ureteral stricture. This system assists surgical procedures by accurately detecting strictures through real-time image analysis. Future research aims to provide both conservative and flexible boundary estimates for determining the stricture location.