Surgical LAparoscopic Motions

Laparoscopic surgery has been widely used in various surgical fields due to its minimally invasive and rapid recovery benefits. However, it requires surgeons to have exceptional technical expertise to overcome challenges such as limited surgical space, restricted visual feedback, and the need for high precision. The rapid development of computer vision and deep learning in surgical action recognition provides essential technical support for improving surgical navigation, training, and postoperative evaluation. Despite this progress, existing publicly available datasets have limited scale, high homogeneity, and inconsistent labeling quality, making it challenging to meet the needs of deep learning models. To address the above issues, we developed the dataset (Surgical LAparoscopic Motions), which encompasses various surgical types. The dataset includes annotations for seven key actions: Abdominal Entry, Use Clip, Hook Cut, Suturing, Panoramic View, Local Panoramic View, and Suction. Experienced medical experts meticulously reviewed the annotations to ensure high quality and clinical accuracy. This dataset aims to promote the development of laparoscopic surgical action recognition and artificial intelligence-driven surgery, supporting intelligent surgical robots and surgical automation.