Generating Rare Surgical Events Using CycleGAN: Addressing Lack of Data for Artificial Intelligence Event Recognition.

ABSTRACT

INTRODUCTION: Artificial Intelligence (AI) has shown promise in facilitating surgical video review through automatic recognition of surgical activities/events. There are few public video data sources that demonstrate critical yet rare events which are insufficient to train AI for reliable video event recognition. We suggest that a generative AI algorithm can create artificial massive bleeding images for minimally invasive lobectomy that can be used to augment the current lack of data in this field. MATERIALS AND METHODS: A generative adversarial network (GAN) algorithm was used (CycleGAN) to generate artificial massive bleeding event images. To train CycleGAN, six videos of minimally invasive lobectomies were utilized from which 1819 frames of nonbleeding instances and 3178 frames of massive bleeding instances were used. RESULTS: The performance of the CycleGAN algorithm was tested on a new video that was not used during the training process. The trained CycleGAN was able to alter the laparoscopic lobectomy images according to their corresponding massive bleeding images, where the contents of the original images were preserved (e.g., location of tools in the scene) and the style of each image is changed to massive bleeding (i.e., blood automatically added to appropriate locations on the images). CONCLUSIONS: The result could suggest a promising approach to supplement the lack of data for the rare massive bleeding event that can occur during minimally invasive lobectomy. Future work could be dedicated to developing AI algorithms to identify surgical strategies and actions that potentially lead to massive bleeding and warn surgeons prior to this event occurrence.