Evaluating drilling and suctioning technique in a mastoidectomy simulator.
This paper presents several new metrics related to bone removal and suctioning technique in the context of a mastoidectomy simulator. The expertise with which decisions as to which regions of bone to remove and which to leave intact is evaluated by building a Naïve Bayes classifier using training data from known experts and novices. Since the bone voxel mesh is very large, and many voxels are always either removed or not removed regardless of expertise, the mutual information was calculated for each voxel and only the most informative voxels used for the classifier. Leave-out-one cross validation showed a high correlation of calculated expert probabilities with scores assigned by instructors. Additional metrics described in this paper include those for assessing smoothness of drill strokes, proper drill burr selection, sufficiency of suctioning, two-handed tool coordination, and application of appropriate force and velocity magnitudes as functions of distance from critical structures.
Sewell
Christopher
C
Department of Computer Science, Stanford University, USA.
Morris
Dan
D
Blevins
Nikolas H
NH
Barbagli
Federico
F
Salisbury
Kenneth
K
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D008416:Mastoid / Q000601:surgery*; D013514:Surgical Procedures, Operative; D014481:United States; D014584:User-Computer Interface