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.
Keywords
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Article info
Publication history
Published online: November 25, 2022
Accepted:
November 6,
2022
Received in revised form:
October 16,
2022
Received:
March 8,
2022
Identification
Copyright
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