Characterization of Surgical Actions in Mastoidectomy

Mastoidectomy is a core surgical procedure in otologic procedures with different goals (e.g., cochlea implantation, removal of cholesteatoma), and between surgeons (who may have different style), all surgeons use a finite armamentarium of surgical actions to

procedure. In this work, we model the mastoidectomy procedure as a compilation of basic surgical actions (called as Action Primitives, APs). These are AP1 (“rough boundary exploration”), followed by AP3 (“fine boundary exploratio and then by AP2 (“obliteration of tissue within the boundaries”). We sought to identify the fundamental surgical actions

constituting mastoidectomy and determine transition boundaries between those APs. Here we present a novel approach as a step to potentially automate certain components of mastoidectomy surgery. We developed methods for parsing raw data (position and orientation of the surgical tool and end effector force) into a sequence of surgical actions (APs) that can be used by a robot in the future. For each AP, we performed preliminary feature extraction and then dimensionality reduction (using Principle Component Analysis). This was followed by boundary detection (using a trained Bayesian Information Criterion algorithm) and identification of dtemporal bone specimens were utilized for this study. Initial results based on experiments with the temporal bones were promising. We hope that the proposed approach will open up the possibility of establishing a standard operating strategy that a robot can use to perform the surgery.