Automated Virtual Coach for Surgical Training

Abstract

Surgical educators have recommended individualized coaching for acquisition, retention and improvement of expertise in technical skills. Such one-on-one coaching is limited to institutions that can afford surgical coaches and is certainly not feasible at national and global scales. We hypothesize that automated methods that model intraoperative video, surgeon's hand and instrument motion, and sensor data can provide effective and efficient individualized coaching. With the advent of instrumented operating rooms and training laboratories, access to such large scale intra-operative data has become feasible. Previous methods for automated skill assessment present an overall evaluation at the task/global level to the surgeons without any directed feedback and error analysis. Demonstration, if at all, is present in the form of fixed instructional videos, while deliberate practice is completely absent from automated training platforms. We believe that an effective coach should: demonstrate expert behavior (how do I do it correctly), evaluate trainee performance (how did I do) at task and segment-level, critique errors and deficits (where and why was I wrong), recommend deliberate practice (what do I do to improve), and monitor skill progress (when do I become proficient).

In this thesis, we present new methods and solutions towards these coaching interventions in different training settings viz. virtual reality simulation, bench-top simulation and the operating room. First, we outline a summarizations-based approach for surgical phase modeling using various sources of intra-operative procedural data such as – system events (sensors) as well as crowdsourced surgical activity context. We validate a crowdsourced approach to obtain context summarizations of intra-operative surgical activity. Second, we develop a new scoring method to evaluate task segments using rankings derived from pairwise comparisons of performances obtained via crowdsourcing. We show that reliable and valid crowdsourced pairwise comparisons can be obtained across multiple training task settings. Additionally, we present preliminary results comparing inter-rater agreement in relative ratings and absolute ratings for crowdsourced assessments of an endoscopic sinus surgery training task data set. Third, we implement a real-time feedback and teaching framework using virtual reality simulation to present teaching cues and deficit metrics that are targeted at critical learning elements of a task. We compare the effectiveness of this real-time coach to independent self-driven learning on a needle passing task in a pilot randomized controlled trial. Finally, we present an integration of the above components of task progress detection, segment-level evaluation and real-time feedback towards the first end-to-end automated virtual coach for surgical training.