Prediction of contact mechanics in metal-on-metal Total Hip Replacement for parametrically comprehensive designs and loads

Donaldson, F.E.; Nyman, Edward; Coburn, James

doi:10.1016/j.jbiomech.2015.04.037

Cited by 10 publications

(6 citation statements)

References 40 publications

(64 reference statements)

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“…It is important to emphasize that the computational studies below are not of a specific manufacturer's device, but of generic devices where the study results have broad impact in that device domain and are translatable to other domains. These models include implantable cardiovascular stents for assessing different methods to calculate fatigue safety factor ( 12 ); heart valves implanted with non-circular configurations ( 13 ) to assess the impact on stresses and strains; inferior vena cava filters to demonstrate a new method for computing embolus transport ( 14 ); hip implants for evaluating the impact of the design on contact mechanics ( 15 ); radiofrequency coils for MRI systems ( 16 , 17 ) to investigate the design parameters on the electromagnetic field; surgical facemasks ( 18 ) for evaluating aerosol leakage of different designs; blood pump ( 19 ) for assessing the ability to predict hemolysis using computational fluid dynamics; and electrical stimulation of implanted lead wires ( 20 ) to investigate local heating. They have also developed new methods for simulating photon transport of x-ray emitters ( 21 ) and compressive sensing for imaging systems ( 22 ).…”

Section: Computational Modeling Researchmentioning

confidence: 99%

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

et al. 2018

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Protecting and promoting public health is the mission of the U.S. Food and Drug Administration (FDA). FDA's Center for Devices and Radiological Health (CDRH), which regulates medical devices marketed in the U.S., envisions itself as the world's leader in medical device innovation and regulatory science–the development of new methods, standards, and approaches to assess the safety, efficacy, quality, and performance of medical devices. Traditionally, bench testing, animal studies, and clinical trials have been the main sources of evidence for getting medical devices on the market in the U.S. In recent years, however, computational modeling has become an increasingly powerful tool for evaluating medical devices, complementing bench, animal and clinical methods. Moreover, computational modeling methods are increasingly being used within software platforms, serving as clinical decision support tools, and are being embedded in medical devices. Because of its reach and huge potential, computational modeling has been identified as a priority by CDRH, and indeed by FDA's leadership. Therefore, the Office of Science and Engineering Laboratories (OSEL)—the research arm of CDRH—has committed significant resources to transforming computational modeling from a valuable scientific tool to a valuable regulatory tool, and developing mechanisms to rely more on digital evidence in place of other evidence. This article introduces the role of computational modeling for medical devices, describes OSEL's ongoing research, and overviews how evidence from computational modeling (i.e., digital evidence) has been used in regulatory submissions by industry to CDRH in recent years. It concludes by discussing the potential future role for computational modeling and digital evidence in medical devices.

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Section: Computational Modeling Researchmentioning

confidence: 99%

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

et al. 2018

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“…Multivariate Linear Regression [18,19], Bayesian modelling [20], Artificial Neural Networks [18,21,22], Random Forest [23] and Kriging [24][25][26], either for linear problems, (e.g., assessment of femoral neck fracture during a single load case [18]) or for non-linear problems (e.g., modelling the contact between bone and implant [19]). Most studies predict a single scalar outcome, such as joint moments and muscle forces [27]; contact forces and contact pressure [21,25,26,[28][29][30]; femoral neck strain and fracture load [18]; implant micromovement and stress shielding [20,31].…”

Section: A Variety Of Surrogate Models Have Been Used By the Biomechamentioning

confidence: 99%

Efficacy and efficiency of multivariate linear regression for rapid prediction of femoral strain fields during activity

Ziaeipoor

Martelli

Pandy

et al. 2019

Medical Engineering & Physics

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Multivariate Linear Regression-based (MLR) surrogate models were explored to reduce the computational cost of predicting femoral strains during normal activity in comparison with finite element analysis. The musculoskeletal model of one individual, the finite-element model of the right femur, and experimental force and motion data for normal walking, fast walking, stair ascent, stair descent, and rising from a chair were obtained from a previous study. Equivalent Von Mises strain was calculated for 1000 frames uniformly distributed across activities. MLR surrogate models were generated using training sets of 50, 100, 200 and 300 samples. The finite-element and MLR analyses were compared using linear regression. The Root Mean Square Error (RMSE) and the 95 th percentile of the strain error distribution were used as indicators of average and peak error. The MLR model trained using 200 samples (RMSE < 108 µε; peak error < 228 µε) was used as a reference. The finiteelement method required 66 secs per frame on a standard desktop computer. The MLR model required 0.1 sec per frame plus 1848 secs of training time. RMSE ranged from 1.2% to 1.3% while peak error ranged from 2.2% to 3.6% of the maximum micro-strain (5020 με). Performance within an activity was lower during early and late stance, with RMSE of 4.1% and peak error of 8.6% of the maximum computed micro-strain. These results show that MLR surrogate models may be used to rapidly and accurately estimate strain fields in long bones during daily physical activity.

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“…Performing computational modelling alongside controlled simulator testing can provide insight into the mechanisms by which damage happens, and aid in designing devices which are better able to withstand edge loading conditions. Static modelling studies have considered deformation and damage and have shown that edge loading led to increased plastic strain [13][14][15], increased contact pressures [13][14][15][16][17], and increased liner peak von Mises stress [15]. It is also possible that edge loading contributes to disassociation of the acetabular cup from its fixation, with modelling showing the highest torque on the shell occurring at the highest separation [18].…”

Section: Introductionmentioning

confidence: 99%

Importance of dynamics in the finite element prediction of plastic damage of polyethylene acetabular liners under edge loading conditions

Jahani

Etchels

Wang

et al. 2021

Medical Engineering & Physics

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Prediction of contact mechanics in metal-on-metal Total Hip Replacement for parametrically comprehensive designs and loads

Cited by 10 publications

References 40 publications

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Advancing Regulatory Science With Computational Modeling for Medical Devices at the FDA's Office of Science and Engineering Laboratories

Efficacy and efficiency of multivariate linear regression for rapid prediction of femoral strain fields during activity

Importance of dynamics in the finite element prediction of plastic damage of polyethylene acetabular liners under edge loading conditions

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