A Novel Approach for Automated Renal Stone Detection from KUB Radiographs in Thai Population

This study presents the implementation and evaluation of YOLOv11x, a deep learning object detection algorithm, for automated renal stone detection on kidney-ureterbladder (KUB) radiographs. A dataset comprising 1,081 KUB radiographs (523 containing renal stones, 558 without) from a Thai population cohort was partitioned into training (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{n}=881$</tex>), validation (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{n} \boldsymbol{=} \mathbf{1 0 0}$</tex>), and testing (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{n} \boldsymbol{=} \mathbf{1 0 0}$</tex>) sets. The model was developed using transfer learning from COCO-pretrained weights with hyperparameters optimized specifically for urolithiasis detection. Quantitative performance assessment demonstrated precision of 90.82 %, sensitivity of 84.76 %, and an F1-score of 87.68% at an Intersection over Union threshold of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 3 5}$</tex>. The detection-based approach exhibited superior performance compared to conventional patch-based classification methods, particularly in precision metrics. Notably, this represents the first implementation of YOLOv11x for renal stone detection in KUB radiographs within a Thai population. The results establish the efficacy of object detection frameworks for clinical screening applications in resource-limited settings where radiation exposure and cost constraints render computed tomography suboptimal as a primary diagnostic modality.