Resumen:
Purpose: Recent innovations in robotics have enabled the development of automatic bone drilling tools which allows surgeons toimprove the precision of their surgical operations. However, these tools still lack valuable tactile information about the material proper-ties of the bone, preventing surgeons from making decisions while operating. The aim of this work is to explore whether robotic drillingtools can infer bone condition on the basis of certain key measures, particularly thrust force. Methods: To infer material properties inrobotic bone drilling processes 1) a complete database of experimental operations with an automatic bone drilling tool is implementedand 2) binary logistic regression models are developed to estimate the type of material from the observed values (mainly the centraltendency of the thrust force). This work compares three different materials: bovine bone specimens, porcine bone specimens and Full-Cure 720, which is a general-purpose resin with, a priori, much less feed resistance. The DRIBON automatic bone drilling tool developedat CEIT is used for the experiments. Results: The classification matrices derived using the logistic models show that it is possible torecognize a bovine bone vs. a porcine bone with a relatively high success rate rate (approximately 90%). In contrast, it is possibleto recognize bone material vs. another material (in our case a resin) with a 100% of success.
