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Boutros et al. Art Int Surg 2022;2:213-23 https://dx.doi.org/10.20517/ais.2022.32 Page 217

Figure 1. Overview of applications of AI in HPB surgery.

the advancement of voice recognition models that can increase the efficiency of patient encounters through
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automated documentation or decision support .

Computer vision
Computer vision, the field concerning itself with machine understanding of visual inputs, has been a useful
tool in the advancement of AI applications in HPB surgery. With applications ranging from identification of
operative steps and instruments on the field to automated labeling of operative steps in surgical videos,
recent approaches to computer vision in surgery have most commonly utilized deep learning, more
specifically convolutional neural networks (CNN), to achieve its aims . CNNs use filters to, in effect,
[14]
summarize the information contained in a given part of the data. For example, a data input that is 4 × 4
units can be processed by a filter that passes over the image as a 2 × 2 block that slides over the data and
[1]
extracts weights [Figure 2]. Multiple convolutional layers can be utilized together to process complex data .
Kenngott et al. in 2014 explored an “augmented reality” system that used robotic C arm cone beam
computed tomography (CT) to help identify hepatic tumors and aid in achieving oncologic resection during
laparoscopic liver surgery. By combining fiducial markers with CT images, they demonstrated precise
identification of the location of hepatocellular carcinoma that could be used to help guide resection .
[15]
Although Kenngott’s study did not utilize computer vision, there are certainly applications of computer
vision in this realm. As computer vision technology and physics modeling of soft-tissue deformations
continue to improve, techniques that do not require the use of fiducial markers have been developed to
further assist in the intraoperative utilization of axial imaging overlays to augment real-time surgical
planning [16,17] .

Perhaps the most investigated application of computer vision in HPB surgery is laparoscopic
cholecystectomy. Mascagni et al. (2021) created a program, EndoDigest, which could identify critical events
in surgical videos and generate clips documenting acquisition of the critical view of safety during a
laparoscopic cholecystectomy . By identifying relevant portions of the case from video recordings,
[18]
EndoDigest was able to successfully isolate a short segment video clip in which the critical view of safety was
obtained in 91% of the test videos in the dataset . Such automatic indexing of surgical procedures can play
[18]
an important role in improving the efficiency of video review and also enables other algorithms to be