Kidney stone detection is essential for timely diagnosis and treatment, and the use of computer vision techniques has significantly improved this process. This study compares the performance of two advanced object detection models, YOLOv8 and YOLOv10, applied to kidney stone detection in CT scan images. YOLOv8, known for balancing speed and accuracy, incorporates the C2F building block for efficient feature extraction. YOLOv10 introduces NMS-free training, which eliminates the need for non-maximum suppression, resulting in faster inference and improved detection efficiency. We trained and evaluated both models using a dataset of annotated medical images, measuring their performance based on accuracy, precision, recall, and inference time. YOLOv10 outperformed YOLOv8 in terms of accuracy and precision, while YOLOv8 showed faster training convergence. The findings of this study provide valuable insights into selecting appropriate models for real-time medical imaging applications, depending on accuracy and resource requirements.