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Multiobjective optimization design of spinal pedicle screws using neural networks and genetic algorithm: mathematical models and mechanical validation.

ABSTRACT: Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE) analytical results based on an L25 orthogonal array, bending and pullout objective functions were developed by an artificial neural network (ANN) algorithm, and the trade-off solutions known as Pareto optima were explored by a genetic algorithm (GA). The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related to those of experimental tests with a correlation coefficient of -0.91 for bending and 0.93 for pullout (P < 0.01 for both). The optimal design had significantly higher fatigue life (P < 0.01) and comparable pullout strength as compared with commercial screws. Multiobjective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously.

SUBMITTER: Amaritsakul Y

PROVIDER: S-EPMC3747468 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Multiobjective optimization design of spinal pedicle screws using neural networks and genetic algorithm: mathematical models and mechanical validation.

Amaritsakul Yongyut Y Chao Ching-Kong CK Lin Jinn J

Computational and mathematical methods in medicine 20130731

Short-segment instrumentation for spine fractures is threatened by relatively high failure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE) analytical results based on an L25 orthogona ...[more]

PMID: 23983810

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Project description:The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans.A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front.The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12-15 plans, any random plan selected from a MOEA population had a 11.3% +/- 0.7% chance of dominating any random plan selected by a standard genetic package with 0.04% +/- 0.02% chance of domination in reverse. By implementing domination advantage and protocol objectives, small and diverse populations of clinically acceptable plans that approximated the Pareto front could be generated in a fraction of 1 h. Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for multiobjective optimizations.The MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. The final goal is to improve practical aspects of the algorithm and integrate it with a decision analysis tool or human interface for selection of the IMRT plan with the best possible balance of successful treatment of the target with low OAR dose and low risk of complication for any specific patient situation.

Project description:Background The incidence of cancer patients with bone metastasis is increasing annually. With the advancement of medical treatment for malignant tumors, the survival time of patients with spinal metastases is gradually being prolonged, and adjacent segment vertebral metastases often occur after conventional pedicle screw (CPS) surgery, leading to spinal instability, pain and nerve function injury again, with repeated symptoms. Combined pedicle screw fixation can maintain or reconstruct the spinal stability. This study aimed to investigate the efficacy and safety of cement-augmented fenestrated pedicle screws in the posterior approach for spinal metastases by comparing with CPS. Methods From January 2017 to August 2019, 52 patients with spinal metastases who underwent separation surgery and internal fixation via posterior approach were retrospectively enrolled. Cases were divided into the cement-augmented pedicle screw (CAPS) group (28 cases) and the CPS group (24 cases). The baseline data [age, gender, surgical sites, surgical segment, Tomita classification, Tomita score, Tokuhashi score, spinal instability neoplastic score (SINS)], surgical information, and local progression-free survival (PFS) time were compared between the two groups. Every patient was followed-up every 3 months with imaging examination. The visual analog scale (VAS) score and Frankel grade of the two groups were recorded before and 3 months after the operation were used to evaluate the efficacy. The operation time, the amount of intraoperative blood loss, the amount of bone cement injected in the pedicle screw group, and the complications of the surgery were recorded to evaluate the safety of CAPS. Results The baseline characteristics were comparable between the two group. Compared with the CPS group, the CAPS group showed significantly longer operation time (163±20 vs. 138±18 min, P<0.001) and lower VAS scores (2.93±1.33 vs. 4.17±1.34, P=0.002). Adjacent segment vertebral metastasis occurred in 10 cases (2 in the CAPS group and 8 in the CPS group, P=0.017). Internal implant failure occurred in 8 cases (1 in the CAPS group and 7 in the CPS group, P=0.011). Compared with the CPS group, the CAPS group had a significantly longer local PFS time (P<0.05). Conclusions CAPS could be a safe and effective choice in surgery for spinal metastases with the posterior approach.

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Multiobjective optimization design of spinal pedicle screws using neural networks and genetic algorithm: mathematical models and mechanical validation.

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Multiobjective optimization design of spinal pedicle screws using neural networks and genetic algorithm: mathematical models and mechanical validation.

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