ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2020
Bioimpedance Sensing Surgical Drill - Computational Modelling and Experimental Validation.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
Funding
Abstract
Surgical drilling to fixate dental implants is associated with high risk of injury to the inferior alveolar nerve (IAN) and the maxillary sinus. Current common practice is to use pre-operative radiographs to plan and drill with no real-time feedback of drill tip position with respect to these critical structures. Real-time proximity sensing of the IAN and maxillary sinus by measuring the electrical impedance properties of tissues, directly from the drill tip, while drilling may reduce and eventually eliminate this risk. Sensing impedance to detect tissue boundaries needs sensor geometry optimization for maximum detection distance. We have created a finite element method (FEM) based simulation platform that yields accurately impedances for different conductivities, frequencies and sensor geometries.
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