Estimation of the stapes-bone thickness in the stapedotomy surgical procedure using a machine-learning technique

Authors:
V. G. Kaburlasos;V. Petridis;P. N. Brett;D. A. Baker
Affiliations:
Dept. of Electr. & Comput. Eng., Aristotelian Univ. of Thessaloniki, Greece;-;-;-
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
1999

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Abstract

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 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-\σ Fuzzy Lattice Neurocomputing (d\σ-FLN) scheme for classification, within the framework of fuzzy lattices. The successful application of the d\σ-FLN scheme is demonstrated in estimating the thickness of a stapes bone "on-line" using drilling data obtained experimentally in the laboratory.