Purpose Traditional ultrasonic detection for kidney stones is prone to variability due to the experience and skill of the operating physician, leading to inconsistent diagnostic outcomes. Factors such as stone size, location, and surrounding tissues can further compromise accuracy. Artificial intelligence (AI), with its advanced data processing and deep learning capabilities, offers a solution by automating the analysis of medical images to improve diagnostic accuracy and efficiency. Methods This cross-sectional study was conducted at our hospital between 01/06/2022 and 03/07/2024., using a datasets of 791 ultrasound images from patients with confirmed kidney stones and 117 images from non-stone participants. The High-Resolution Network (HRNet) was employed for model training to detect kidney stones in ultrasound images. Detailed image annotations were created using the labelme tool and converted to COCO format for compatibility with AI algorithms. Results The HRNet model achieved an accuracy of 86.4% in detecting kidney stones in the validation datasets, with a sensitivity of 94.3% and specificity of 73.2%. The model effectively identified both kidney stones and normal conditions. Conclusions The study demonstrates that AI, specifically HRNet, can significantly enhance the accuracy and efficiency of kidney stone detection in ultrasound imaging. This approach reduces the diagnostic burden on physicians and improves patient outcomes. However, challenges remain, including the need for large-scale annotated datasets and rigorous validation of AI models to ensure reliability in clinical settings.