Artificial Intelligence in Urolithiasis Imaging: Radiographic Detection of Urinary Stones in Resource-Constrained Settings

This study aims to develop an AI-powered detection model specifically designed for X-ray modalities to enhance the diagnosis of urinary tract stones. Urinary tract stones are a prevalent medical condition that can lead to significant morbidity and healthcare costs. Traditional diagnostic methods, such as CT scans, while effective, are often expensive and may not be accessible to all patients. Therefore, this research focuses on leveraging artificial intelligence to improve the accuracy and efficiency of X-ray imaging in identifying urinary tract stones.We conducted a retrospective observational study, analyzing a comprehensive dataset of X-ray images from patients diagnosed with urinary tract stones. The AI model was trained using advanced machine learning algorithms to recognize patterns and features indicative of stone presence. Performance metrics, including true positive, true negative,false negative, false positive, and accuracy, were evaluated to assess the model's effectiveness compared to conventional diagnostic methods.The results demonstrate that the AI-powered model significantly improves diagnostic accuracy while maintaining cost-effectiveness. This approach not only enhances patient outcomes by facilitating timely diagnosis but also promotes the use of widely available X-ray technology, making it a viable option for healthcare systems with limited resources. Our findings suggest that future research should continue to explore AI applications in medical imaging, focusing on developing affordable and accessible tools for broader community use.