CS Masters student works with a local surgeon to improve surgical training methods
When it comes to doing surgery, having some hands-on training before you hit the operating room just makes sense. Technology is now able to mimic certain surgical settings to make this happen.
However, evaluating the performance of a surgeon is a difficult – especially when dealing with minimally invasive surgery where the movements are precise and the view of the instruments is obscured.
Currently, resident surgeons are observed and evaluated by experts in their field. As this method is quite subjective, research is being done to incorporate more quantitative measures. The total time for completion (an expert would be able to finish a procedure faster than a resident) and motion smoothness (an expert’s movements would be more fluid than a resident’s), have been studied, but these are still very general assessments.
Fraser’s research uses sensors attached to the candidate to precisely break down the sub-movements of the instruments, including their orientation and the amount of force applied. These measurements can then be analyzed and compared to those of an expert surgeon, giving an accurate assessment of surgical skill.
Particular segments of the procedure can be extracted and evaluated individually as well (e.g. Your needle insertion is 81% similar to an expert’s, however, on your knot tying the similarity drops to 27%), revealing specific areas in need of improvement.
This objective grading system makes the feedback unbiased and much more useful for surgical trainees, pushing computer-assisted surgical training research one step closer to reality.
Article and photos, 2010.