Abstract
Introduction
Video-based review of surgical procedures has proven to be useful in training by enabling
efficiency in the qualitative assessment of surgical skill and intraoperative decision-making.
Current video segmentation protocols focus largely on procedural steps. Although some
operations are more complex than others, many of the steps in any given procedure
involve an intricate choreography of basic maneuvers such as suturing, knot tying,
and cutting. The use of these maneuvers at certain procedural steps can convey information
that aids in the assessment of the complexity of the procedure, surgical preference,
and skill. Our study aims to develop and evaluate an algorithm to identify these maneuvers.
Methods
A standard deep learning architecture was used to differentiate between suture throws,
knot ties, and suture cutting on a data set comprised of videos from practicing clinicians
(N = 52) who participated in a simulated enterotomy repair. Perception of the added
value to traditional artificial intelligence segmentation was explored by qualitatively
examining the utility of identifying maneuvers in a subset of steps for an open colon
resection.
Results
An accuracy of 84% was reached in differentiating maneuvers. The precision in detecting
the basic maneuvers was 87.9%, 60%, and 90.9% for suture throws, knot ties, and suture
cutting, respectively. The qualitative concept mapping confirmed realistic scenarios
that could benefit from basic maneuver identification.
Conclusions
Basic maneuvers can indicate error management activity or safety measures and allow
for the assessment of skill. Our deep learning algorithm identified basic maneuvers
with reasonable accuracy. Such models can aid in artificial intelligence-assisted
video review by providing additional information that can complement traditional video
segmentation protocols.
Keywords
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Article info
Publication history
Published online: November 24, 2022
Accepted:
October 19,
2022
Received in revised form:
October 14,
2022
Received:
March 29,
2022
Identification
Copyright
© 2022 Elsevier Inc. All rights reserved.
