The insights on the simple semantic structure of surgical videos can be leveraged to design more efficient data collection and annotation strategies for building surgical video datasets. One approach could be to prioritize the annotation of key frames that represent critical surgical phases or moments where the presence of specific tools is essential. By focusing on annotating key frames that are most informative and representative of the surgical procedure, the annotation process can be optimized to provide high-quality training data without the need to label every frame in the video. Additionally, leveraging the simple semantic structure of surgical videos can guide the selection of training data that maximizes the diversity and coverage of different surgical phases and tool interactions, leading to more effective model training and performance.