Estimating the mechanical competence parameter of the trabecular bone: a neural network approach

Filletti, Érica Regina; Roque, Waldir L.

doi:10.1590/2446-4740.05615

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…This creates the opportunity to explore different methods of evaluation of the maximum stress in the pedicular screw positioning. Artificial neural network (ANN) is one method that has been used to determine the mechanical structure of bones [5]. It has also been shown that this model can be trained to reproduce results from finite element analysis of pedicle screws [6].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of pedicle screw position using finite element method and neural networks

Silva

Corso

Costa

2021

J Braz. Soc. Mech. Sci. Eng.

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The application of pedicle screws in instrumentation of the spine is widely used in spine correction surgeries. High stresses can be developed in the region surrounding the screw, and it is important to understand how to minimize this stress in order to reduce risk of patient harm. Traditional studies have used FEM (finite element method) to evaluate the best position and orientation of the pedicle screws; however, such approach demands iterative simulations to find the minimum stress position. This work has used an optimization-based method, based on neural network and genetic algorithm, to test different pedicular screw positions. By performing simulations of different screw positions through finite element analysis and using the results to train an artificial neural network, a less computationally intensive method of determining stress was found. The mathematical model created was optimized through a genetic algorithm and used to find a trajectory that results in lower mechanical stress between the pedicle screw and the vertebra. The results of the ANN approach show a reduction of 5.25% in the von Mises stress when compared to the FEA. Regarding the optimal trajectory, the angles differences were 2° in sagittal plane and 1.4° in transverse plane, hence showing good results.

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Section: Introductionmentioning

confidence: 99%

“…An activation function is used to introduce nonlinearity to the model [17]. This model has been used to emboss the mechanical stress in the bone and biomechanical systems such as pedicle screw fixation [5,6,13,18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of pedicle screw position using finite element method and neural networks

Silva

Corso

Costa

2021

J Braz. Soc. Mech. Sci. Eng.

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“…The main advantage of ANNs is their ability to generalize or learn from examples 17 ; that is, ANNs can generalize learned information to provide satisfactory results for cases not seen in training. Therefore, ANNs have been used in many fields, such as chemistry 18 , geology 19 , medicine 20 , neurocomputations 21 and biomedical engineering 22 , among others.…”

Section: Introductionmentioning

confidence: 99%

Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

2020

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Dry grains from leguminous species, such as soybeans (Glycine max L.), common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.) and corn (Zea mays L.), are regularly consumed for human nutrition. This paper showed the possibility of estimating bulk density as quality parameter of 4 different dry grains (soybeans, common beans, chickpeas and corn) in a same model using the average values of color descriptors from digital images combined with an artificial neural network, with low computational costs. These food products are good sources of carbohydrates, protein and dietary fiber, and they possess significant amounts of vitamins and minerals and a high energetic value. Estimation of the physicochemical properties of grains is challenging due to variations in shape, texture, and size and because the grain colors appear similar to the naked eye. In this work, an analytical method was developed based on digital images converted into ten color scale descriptors combined with a neural model to provide an accurate parameter for grain quality control with a low computational cost. The bulk densities of four type of grains, i.e., soybeans, beans, chickpeas and corn, were predicted using numerical data represented by the average values of color histograms of a ten color scale (red - R, green - G, blue - B, hue - H, saturation - S, value - V, relative RGB and luminosity - L) from digital images combined with artificial neural networks (ANNs). The reference bulk densities were empirically measured. A very good correlation between the reference values and values predicted by the ANN was achieved, and with a single ANN developed for the four grains, a correlation coefficient of 0.98 was observed for the test set. Moreover, the relative errors were between 0.01 and 5.6% for the test set.

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Commonality in the microarchitecture of trabecular bone: A preliminary study

Zhang

Kirby

Roy

et al. 2018

Bone

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Estimating the mechanical competence parameter of the trabecular bone: a neural network approach

Cited by 3 publications

References 10 publications

Optimization of pedicle screw position using finite element method and neural networks

Optimization of pedicle screw position using finite element method and neural networks

Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

Commonality in the microarchitecture of trabecular bone: A preliminary study

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