The effect of three-dimensional shape optimization on the probabilistic response of a cemented femoral hip prosthesis

Nicolella, Daniel P.; Thacker, Ben H.; Katoozian, Hamid; Davy, Dwight T.

doi:10.1016/j.jbiomech.2005.03.010

Cited by 60 publications

(55 citation statements)

References 24 publications

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“…Design optimization of implants in bone at macroscale by using deterministic computational models has also received increased attention (see for instance [36,43,50,53,56,74]). However, there are only a few works in the framework of the optimization under uncertainties in biomechanics (see for instance [34,65,67,73]). Nevertheless, this type of works does not exactly correspond to the framework of the present paper that concerns the optimization of the geometry of a mesoscale implant in a cortical bone that must be modeled by a heterogeneous random elastic medium at this scale.…”

Section: Design Optimization Of Implants In Bonementioning

confidence: 99%

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Soize

2017

Comput Mech

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This paper deals with the optimal design of a titanium mesoscale implant in a cortical bone for which the apparent elasticity tensor is modeled by a non-Gaussian random field at mesoscale, which has been experimentally identified. The external applied forces are also random. The design parameters are geometrical dimensions related to the geometry of the implant. The stochastic elastostatic boundary value problem is discretized by the finite element method. The objective function and the constraints are related to normal, shear, and von Mises stresses inside the cortical bone. The constrained nonconvex optimization problem in presence of uncertainties is solved by using a probabilistic learning algorithm that allows for considerably reducing the numerical cost with respect to the classical approaches.

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Section: Design Optimization Of Implants In Bonementioning

confidence: 99%

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Soize

2017

Comput Mech

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“…First principal stress of the cement mantle was used as a measure of tensile failure of cement (Harrington et al 2002;Nicolella et al 2006). Depending upon composition and curing, the ultimate tensile strength and compressive strength of cement have been reported to range from 24 to 49 MPa and 73 to 117 MPa, respectively (Lewis 1997).…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…Another reason for the higher stress and strain in the lateral proximal femur is the relatively low density in this area for this patient. Nicolella et al (2006) performed a probabilistic analysis to assess the effect of three-dimensional (3D) deterministic shape optimisation of a cemented femoral prosthesis on the predicted probability of failure of the prosthesis system. It was found that the uncertainty in the joint loading, cement strength and implant -cement interface strength have the greatest effect on the computed probability of failure.…”

Section: Corroborating This Study Against Existing Workmentioning

confidence: 99%

“…Many studies have been performed to determine the stress distribution in the cement and at the implant -cement interface (Verdonschot and Huiskes 1997;Stolk et al 2001Stolk et al , 2003Pérez et al 2006;Waanders et al 2011). The performance of cemented THR is dependent on a number of factors including patientspecific bone geometry (Jonkers et al 2008), bone material properties (Schileo et al 2007), loading (Huiskes 1990;Pancanti et al 2003), stem design (Nicolella et al 2006;Dopico-Gonzalez et al 2010;Ishida et al 2011), stem positioning (Kleemann et al 2003;Bah et al 2011) and cement mantle thickness (Hernigou et al 2009). …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity analysis of a cemented hip stem to implant position and cement mantle thickness

Shi

Browne

Strickland

et al. 2013

Computer Methods in Biomechanics and Biomedical Engineering

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Patient-specific finite element models of the implanted proximal femur can be built from pre-operative computed tomography scans and post-operative X-rays. However, estimating three-dimensional positioning from two-dimensional radiographs introduces uncertainty in the implant position. Further, accurately measuring the thin cement mantle and the degree of cement -bone interdigitation from imaging data is challenging. To quantify the effect of these uncertainties in stem position and cement thickness, a sensitivity study was performed. A design-of-experiment study was implemented, simulating both gait and stair ascent. Cement mantle stresses and bone -implant interface strains were monitored. The results show that small variations in alignment affect the implant biomechanics, especially around the most proximal and most distal ends of the stem. The results suggest that implant position is more influential than cement thickness. Rotation around the medial -lateral axis is the dominant factor in the proximal zones and stem translations are the dominant factors around the distal tip.

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“…The prediction of a performance envelope rather than a single deterministic result provides information about not only the level of performance, but also the likelihood of a specific level of performance. While studies [18][19][20] have applied optimization to implant design for specific criteria, few studies 21,22 have evaluated design robustness by considering variability in the inputs (e.g., loading, bone properties, and alignment). Robustness is a measure of how consistently a specific implant design performs when considering patient variation, such as loading and constraint variability, and small malalignments in implant position.…”

Section: Introductionmentioning

confidence: 99%

Effects of knee simulator loading and alignment variability on predicted implant mechanics: A probabilistic study

et al. 2006

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Inherent variability in total knee arthroplasty loading and alignment, present in vivo and in simulator testing, may ultimately influence polyethylene tibial insert wear and long-term performance. The effect of this variability was quantified on implant kinematics and contact mechanics during simulated gait loading conditions using semi-constrained and unconstrained fixed bearing, cruciate retaining implants. A probabilistic finite element model of the Stanmore knee wear simulator was utilized to estimate the envelope of anterior-posterior (AP) and internal-external (IE) position and contact pressure and to evaluate the variability in corresponding ranges of motion (ROM). Variability levels were represented by standard deviations of up to 10% of the maximum value for load inputs and 0.25 mm and 0.58 for component alignment inputs. Model predictions compared well with experimental simulator results for the semi-constrained implant, with predicted positional envelopes of up to 1.8 mm (AP) an 3.48 (IE) for the semi-constrained and upto 2.6 mm (AP) and 3.78 (IE) for the unconstrained implant at the variability levels evaluated. ROM varied by up to 22%, while peak contact pressure variations averaged less than 2 MPa for both designs. For each implant, loading variability was more influential during the swing phase of gait, while alignment variability affected kinematics more during stance. The relative rank of sensitivities showed differences between the two designs, providing insight into critical parameters affecting kinematics and contact characteristics. ß

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The effect of three-dimensional shape optimization on the probabilistic response of a cemented femoral hip prosthesis

Cited by 60 publications

References 24 publications

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Sensitivity analysis of a cemented hip stem to implant position and cement mantle thickness

Effects of knee simulator loading and alignment variability on predicted implant mechanics: A probabilistic study

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