Enhancing Surgical Performance in Cardiothoracic Surgery with
  Innovations from Computer Vision and Artificial Intelligence: A Narrative
  Review
Merryn D. Constable Hubert P. H. Shum Stephen Clark
When technical requirements are high, and patient outcomes are critical,
opportunities for monitoring and improving surgical skills via objective motion
analysis feedback may be particularly beneficial. This narrative review
synthesises work on technical and non-technical surgical skills, collaborative
task performance, and pose estimation to illustrate new opportunities to
advance cardiothoracic surgical performance with innovations from computer
vision and artificial intelligence. These technological innovations are
critically evaluated in terms of the benefits they could offer the
cardiothoracic surgical community, and any barriers to the uptake of the
technology are elaborated upon. Like some other specialities, cardiothoracic
surgery has relatively few opportunities to benefit from tools with data
capture technology embedded within them (as with robotic-assisted laparoscopic
surgery, for example). In such cases, pose estimation techniques that allow for
movement tracking across a conventional operating field without using
specialist equipment or markers offer considerable potential. With video data
from either simulated or real surgical procedures, these tools can (1) provide
insight into the development of expertise and surgical performance over a
surgeon's career, (2) provide feedback to trainee surgeons regarding areas for
improvement, (3) provide the opportunity to investigate what aspects of skill
may be linked to patient outcomes which can (4) inform the aspects of surgical
skill which should be focused on within training or mentoring programmes.
Classifier or assessment algorithms that use artificial intelligence to 'learn'
what expertise is from expert surgical evaluators could further assist
educators in determining if trainees meet competency thresholds.