Title

The use of surface strain data and a neural networks solution method to determine lumbar facet joint loads during in vitro spine testing

Document Type

Article

Abstract

A new method for determining facet loads during in vitro spine loading using strain gauges and a neural networks solution method was investigated. A test showed that the new solution method was more robust than and as accurate as a previously presented graphical solution method for computing facet loads using surface strain. The technique was subsequently utilized to assess facet loads at L1-L2 during flexibility testing [7.5 N m pure moments in flexion (FL), extension (EX), right and left axial rotation (AR), and right and left lateral bending (LB)], and stiffness testing (FL-EX with 400 N compressive follower load) of six human lumbar spine segments (T12-L2). In contrast to other techniques, such as thin film sensors or pressure-sensitive film, the strain-gauge method leaves the facet joint capsule intact during data collection, presumably allowing more natural load transmission. During flexibility tests, the mean (±standard deviation) calculated facet loads (in N) were 46.1±41.3 (FL), 51.5±39.0 (EX), 70.3±43.2 (AR-contralateral side), 31.3±33.4 (AR-ipsilateral side), 30.6±29.1 (LB-contralateral side), and 32.0±44.4 (LB-ipsilateral side). During stiffness tests, the calculated facet loads were 45.5±40.4 (upright), 46.6±41.9 (full FL), and 75.4±39.0 (full EX), corresponding to an equivalent of 11.4%, 11.6%, and 18.8% of the compressive follower load (upright, full FL and EX, respectively). The error associated with this technique, which was below 11 N for loads up to 125 N, is comparable to that reported with other techniques. The new method shows promise for assessing facet load during in vitro spine testing, an important parameter when evaluating new implant systems and surgical techniques. © 2008 Elsevier Ltd. All rights reserved.

Publication Date

8-28-2008

Publication Title

Journal of Biomechanics

ISSN

00219290

Volume

41

Issue

12

First Page

2647

Last Page

2653

PubMed ID

18657814

Digital Object Identifier (DOI)

10.1016/j.jbiomech.2008.06.010

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