Transfer Learning of a Temporal Bone Performance Model via Anatomical Feature Registration

Evaluation of the outcome (end-product) of surgical procedures carried out in virtual reality environments is an essential part of simulation-based surgical training. Automated end-product assessment can be carried out by performance classifiers built from a set of expert performances. When applied to temporal bone surgery simulation, these classifiers can evaluate performance on the bone specimen they were trained on, but they cannot be extended to new specimens. Thus, new expert performances need to be recorded for each new specimen, requiring considerable time commitment from time-poor expert surgeons. To eliminate this need, we propose a transfer learning framework to adapt a classifier built on a single temporal bone specimen to multiple specimens. Once a classifier is trained, we translate each new specimens' features to the original feature space, which allows us to carry out performance evaluation on different specimens using the same classifier. In our experiment, we built a surgical end-product performance classifier from 16 expert trials on a simulated temporal bone specimen. We applied the transfer learning approach to 8 new specimens to obtain machine generated end-products. We also collected end-products for these 8 specimens drilled by a single expert. We then compared the machine generated end-products to those drilled by the expert. The drilled regions generated by transfer learning were similar to those drilled by the expert.