Estimation of the stapes-bone thickness in the stapedotomy surgical procedure using a machine-learning technique.
Stapedotomy is a surgical procedure aimed at the treatment of hearing impairment due to otosclerosis. The treatment consists of drilling a hole through the stapes bone in the inner ear in order to insert a prosthesis. Safety precautions require knowledge of the nonmeasurable stapes thickness. The technical goal herein has been the design of high-level controls for an intelligent mechatronics drilling tool in order to enable the estimation of stapes thickness from measurable drilling data. The goal has been met by learning a map between drilling features, hence no model of the physical system has been necessary. Learning has been achieved as explained in this paper by a scheme, namely the d-sigma Fuzzy Lattice Neurocomputing (d sigma-FLN) scheme for classification, within the framework of fuzzy lattices. The successful application of the d sigma-FLN scheme is demonstrated in estimating the thickness of a stapes bone "on-line" using drilling data obtained experimentally in the laboratory.
Kaburlasos
V G
VG
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece.
Petridis
V
V
Brett
P N
PN
Baker
D A
DA
D003638:Deafness / Q000601:surgery*; D017143:Fuzzy Logic; D007858:Learning; D013514:Surgical Procedures, Operative / Q000379:methods*