Given the growing volume of surgical data and the increasing demand for annotation, there is a pressing need to streamline the annotation process for surgical videos. Previously, annotation tools for object detection tasks have greatly evolved, reducing time expense and enhancing ease. There are also many initial frame selection approaches for Artificial Intelligence (AI) assisted annotation tasks to further reduce human effort. However, these methods have rarely been implemented and reported in the context of surgical datasets, especially in cataract surgery datasets. The identification of initial frames to annotate before the use of any tools or algorithms determines annotation efficiency. Therefore, in this paper, we chose to prioritize the development of a method for selecting initial frames to facilitate the subsequent automated annotation process. We propose a customized initial frames selection method based on feature clustering and compare it to commonly used temporal selection methods. In each method, initial frames from cataract surgery videos are selected to train a surgical tool detection model. The model assists in the automated annotation process by predicting bounding boxes for the surgery video objects in the remaining frames. Evaluations of these methods are based on how many edits users need to perform when annotating the initial frames and how many edits users are expected to perform to correct all predictions. Additionally, the total annotation cost for each method is compared. Results indicate that on average, the proposed cluster-based approach requires the fewest total edits and exhibits the lowest total annotation cost compared to conventional methods. These findings highlight a promising direction for developing a complete application, featuring streamlined AI-assisted annotation processes for surgical tool detection tasks.