Providing automated real-time technical feedback for virtual reality based surgical training: Is the simpler the better?

Abstract

In surgery, where mistakes have the potential for dire consequences, proper training plays a crucial role. Surgical training has traditionally relied upon experienced surgeons mentoring trainees through cadaveric dissection and operating theatre practice. However, with the growing demand for more surgeons and more efficient training programs, it has become necessary to employ supplementary forms of training such as virtual reality simulation. However, the use of such simulations as autonomous training platforms is limited by the extent to which they can provide automated performance feedback. Recent work has focused on overcoming this issue by developing algorithms to provide feedback that emulates the advice of human experts. These algorithms can mainly be categorized into rule-based and machine learning based methods, and they have typically been validated through user studies against controls that received no feedback. To our knowledge, no investigations into the performance of the two types of feedback generation methods in comparison to each other have so far been conducted. To this end, we introduce a rule-based method of providing technical feedback in virtual reality simulation-based temporal bone surgery, implement a machine learning based method that has been proven to outperform other similar methods, and compare their performance in teaching surgical skills in practice through a user study. We show that simpler rule-based methods can be equally or more effective in teaching surgical skills when compared to more complex methods of feedback generation.