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Page 218 Boutros et al. Art Int Surg 2022;2:213-23 https://dx.doi.org/10.20517/ais.2022.32

Figure 2. Example of how one layer of a convolutional neural network processes input data. A 4 × 4 unit input is processed by a 2 × 2
filter to extract relevant weights for the task according to the architecture of the network.

selectively activated to help with other tasks. For example, CVSnet, another algorithm from Mascagni et al.
(2022), is an automated method to highlight whether or not a surgeon has achieved the three key
components of the critical view of safety . Furthermore, Madani et al. (2022) created a deep learning
[19]
computer vision model, trained on the annotations of experienced surgeons, to identify safe and unsafe
zones of dissection in the hepatocystic triangle during a laparoscopic cholecystectomy with the aim of
[20]
preventing injury due to visual misperception in the identification of anatomy . Such examples
demonstrate that AI could be used to identify anatomy, with the goal of one day enabling real-time feedback
in the operating room using a constellation of algorithms to aid surgeons and prevent adverse events and
injuries.

Beyond intraoperative decision support at the level of identifying structures or safe and unsafe areas of
dissection, computer vision can help to extract insights about patient characteristics. Ward et al. (2022) used
a combination of computer vision and Bayesian models to identify the Parkland Grading Scale (PGS), a
measure of gallbladder inflammation, and predict the intraoperative course of a laparoscopic
cholecystectomy . In this manner, the authors could investigate whether increasing PGS score was
[21]
associated with differences in the length of operative time, likelihood of attaining the critical view of safety,
or likelihood of injuring the gallbladder and spilling bile. The computer vision model was able to accurately
identify the PGS, and the work otherwise demonstrated that different surgeons were affected to different but
[21]
systematic lengths by the level of inflammation of the gallbladder .

OBSTACLES TO AI IN HPB SURGERY
The sheer volume of publications and discussion on AI in surgery would suggest that these technologies
may be ready for primetime and are an inevitability in clinical practice. The reality, however, is that
significant obstacles remain that prevent the translation of AI research into clinically usable and meaningful