Urolithiasis is a common cause of emergency department visits, and early detection of urinary tract stones is critical to avoid complications, especially in healthcare systems with limited access to advanced imaging modalities. This study aimed to develop and internally evaluate an artificial intelligence (AI) model based on plain abdominal X-ray imaging for the detection of urinary stones in a resource-constrained setting. We conducted a retrospective observational study using radiographic images collected from two hospitals where X-ray is used as an initial diagnostic tool. From an initial collection of 11,900 radiographs, 2,410 images were saved. These images were annotated using the Roboflow platform by two trained study authors in collaboration with a radiology technician. The annotated dataset was partitioned into training (70%, n = 1,687), validation (20%, n = 482), and test (10%, n = 241) subsets, and a YOLOv8-based object detection model was fine-tuned for urinary stone identification. The model displayed modest overall performance, achieving an accuracy of 0.56 (259/459), precision of 0.86 (196/228), recall of 0.53 (196/364), and specificity of 0.66 (63/95). These results indicate that, while the current system does not yet yield high clinical profit in diagnostic settings, it demonstrates the technical feasibility of deploying AI for stone detection using widely available X-ray equipment. This work should be viewed as a proof of concept and a call to develop further and validate AI-based diagnostic tools that are designed to support existing imaging devices in resource-limited environments. Future research should focus on improving sensitivity, specificity, expanding and diversifying training datasets.
Khazma et al. (Sun,) studied this question.