Journal of Mechanics in Medicine and Biology

The mandibular bone may be damaged for a variety of reasons. One of the methods used to facilitate and stimulate the bone to improve hard tissue formation is the use of bone grafts. In this study, a novel methodology was introduced to take a step towards making a custom xenograft for a patient with a mandibular bone defect. The application of the finite element method and evaluation of the graft simulation results was proposed, then the customized xenograft was provided using micro-milling. Also, 3D printing technology was used as a preoperative assessment of bone-graft interface conformity. Afterward, the graft was implemented for mandibular augmentation and the patient was prepared for further dental implantation. Finally, cone-based computer tomography images in different time intervals were taken for clinical assessment. Results showed that six months after the graft placement, the vertical distance from the alveolar ridge to the incisive canal and the mandibular canal was increased by 261% and 250%, respectively. Furthermore, the images taken after the insertion of dental implants and frequent observations by the dental surgeon approved the success of the treatment. Additionally, several quantitative parameters were compared to and established with the previous literature. Combining the conventional clinical examination method with an initial computational simulation by the criteria proposed in this study aided in predicting the success of mandibular augmentation and the subsequent dental implantation. More numerical analysis criteria can be added and assessed in future studies to improve the proposed method.

Breast cancer is the leading cause of cancer death among women. Early identification of breast cancer allows patients to receive appropriate therapy, increasing their chances of survival. However, the early and precise detection of breast cancer is more challenging for researchers. Besides, histopathological image is the most effective tool for precise and early detection of breast cancer. Although it has restricted efficiency, breast cancer detection is the main challenge in medical image analysis. This study develops an Enhanced Cat Swarm Optimization-based Deep Convolutional Neural Network (ECSO-based DCNN) for the classification of breast cancer. Pre-processing is also more crucial in image processing since it improves image quality by removing noise from an input image. The segmentation process is used through a designed deep holoentropy-correlative segmentation method, where significant blood cells are extracted. The breast cancer detection and classification are performed using DCNN, which is trained by devised ECSO algorithm. The execution of the introduced deep holoentropy-correlative blood cell segmentation model with optimized DCNN for breast cancer categorization is performed using BreakHis and Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) datasets. The proposed ECSO-based DCNN model obtained better performance with accuracy, sensitivity, and specificity of 96.26%, 97.6%, and 93.57%.

Although the task-specific robot treadmill therapies can improve gait performance of children with cerebral palsy, not all the institutions have the possibility to access to the sensor robot information to obtain quantitative measures related actual progress of the children. The aim of this study is to propose a computer vision system to investigate the effort of children with cerebral palsy during robot treadmill therapy. Ten participants diagnosed with cerebral palsy underwent single robot therapy sessions consisting of a motivated period (active: kicking a ball), followed by an unmotivated period (passive: simple walking). The outcome measure was the ankle trajectory recorded by a video camera. Indexes based on the ankle trajectory between active and passive therapy were computed: (a) the gait analysis based on the difference on a sample-by-sample manner, and b) the step cycles with dynamic time warping (DTW). Inference statistical analysis shows that the robot trajectories for passive and active therapies with the gait analysis presents statistical differences (p-$\mathrm{\text{value}}<0.01))$. Likewise, DTW showed significant differences in robot trajectories related to passive and active therapy (p-$\mathrm{\text{value}}<0.01))$. We found that the statistical differences are independent of the severity of central gait impairment. This suggests that information about patient involvement could be given to the therapist and could be used to assess patient participation. The method showed the potential to be used as a tool for the assessment the improvement progress.

Background: Minimally invasive surgery is widely used for managing fractures. When using the intramedullary nailing for bone fixation, surgeons must insert interlocking screws to prevent rotation of the bone fragment; however, it is difficult to determine the exact screwing position of intramedullary nails inserted into the bone. Conventionally, the distal interlocking nail surgery is performed under intermittent X-ray shooting. Nevertheless, this freehand fluoroscopic technique is technically demanding and time-consuming. Currently, the failure rate of this surgery is more than $60\%$, and the location error requires to be controlled within 2mm. Purpose: To develop a deep-learning approach for locating the intramedullary nail’s holes based on 2D calibrated fluoroscopic images. Methods: The projection of the hole’s axis is deeply regressed in the first step. Then, the hole’s 3D axis is derived by computing the intersection line of two planes determined by the projection of the axis and the X-ray source, respectively. The benefit of the data-driven manner is that our method can be applied to the arbitrary shape of the hole’s contour. Besides, we extract hole’s contour as the distinctive feature, so as to reduce the space of the training data in a large scale. Results: Our approach is proved to be efficient and easy to be implemented, and it has been compared with traditional location method in phantom experiments. The location accuracy error of the traditional method is $2.35$mm, $2.37^{\circ }$, and the location error of this method is $1.48$mm, $1.41^{\circ }$. Furthermore, the traditional method takes an average of 10min to complete the location, while our method takes only 4min. In addition, to further verify the robustness of our method, we carried out a preclinical study involving different neural networks for locating the hole’s axis. Conclusion: Whether in terms of time consumption or accuracy error, our method is significantly better than traditional method, and the efficiency has been significantly improved. Therefore, our method has great clinical value. In addition, our approach has potential advantages over the X-ray guided freehand solution in terms of radiation exposure, and it has tremendous application prospects.

The proposed research demonstrates an attempt to introduce a systematic procedure for studying the multifractal dynamics of biceps brachii muscle actions during light exercises. The intrinsic patterns in the Surface Electromyogram (sEMG) signals were extracted by fruitfully exploiting the Multifractal features of the signal. The Multifractal features are derived from the multifractal singularity spectrum of the EMG signals. This multifractal feature vector could be utilized for signal characterization, which was successfully extended for the classification of EMG signals. Experimental verification has been done to validate the feature extraction and classification algorithm proposed in this article. A pilot study was conducted on signals from the Physionet database, which was then extended to a medium database developed with biceps brachii EMG signals of 32 healthy male subjects. From this study, we could validate that the Multifractal features fit as a differentiating multi-feature set and also for the progressive assessment of EMG signals of different classes. The observations of the proposed method revealed that the strength of multifractality and area under the spectrum increase as a result of fast movements of the forearm or increases in muscle fatigue. The classification is performed using well-recognized supervised classification algorithms such as k-Nearest Neighbor and Support Vector Machine (SVM) Classifiers. The performance analysis of the classifiers are studied on various measures such as Accuracy, Precision, Recall, F1 score. The statistical significance analysis of the feature vector was carried out by one-way ANOVA test.

Current literature reports a wide range of stiffness values and constitutive models for lung tissue across different spatial scales. Comparing the reported lung tissue stiffness values across different spatial scales may provide insights into how well those mechanical properties and the proposed constitutive models represent lung tissue’s mechanical behavior. Thus, this study applies in silico modeling to compare and potentially bridge the differences reported in lung tissue mechanical properties at different length scales. Specifically, we predicted the mesoscale mechanical behavior of rat lung tissue based on in situ and in vitro microscale test data using finite element (FE) analysis and compared those computational predictions to the reported data using mesoscale uniaxial experiments. Our simulations showed that microscale-based stiffness values differed from the mesoscale data in the simulated strain range of 0–60%, with the atomic force microscopy (AFM)-based data overestimating the mesoscale data above 15% strain. This research demonstrates that computational modeling can be used as an informative and guiding tool to investigate and potentially bridge the differences in reported lung tissue material properties across length scales.

Using the data obtained from the computational fluid dynamics simulations, a back-propagation neural network model was developed to predict the velocity magnitudes and the instantaneous wall shear stresses in two patient-specific aneurysms. The models were also used to determine the effect of the blood composition on the rapture risk of the aneurysms. Based on the possible combination, five back propagation models were developed. The architecture of five models is determined based on number of neurons in the hidden layer. All the models in each algorithm were trained and tested. The accuracy of the developed models was evaluated through statistical analysis of the network output in terms of mean absolute error, root mean squared error, mean squared error, and error deviation. According to the results obtained, all BPA effectively predicted velocity magnitude and instantaneous wall shear stress. Model 1 was, however, less accurate when compared to the other five models, as it had one neuron in its hidden layer. The analysis confirms that the neuron number in the hidden layer play a definitive role in predicting the respective outputs. The performance assessment all of the back-propagation models revealed that the error incurred was acceptable. The algorithms’ training and testing in this study were satisfactory, since the network output was in reasonably good conformity with the target computational fluid dynamics result.

This study conducted a series of biaxial stretching tests on the spinal dura mater. To investigate the specific effects of different loading patterns on mechanical responses of the dura mater, load-controlled asymmetric (longitudinal vs. circumferential loading at 1:0.5 and 0.5:1 ratios) and equi-load (longitudinal vs. circumferential at a 1:1 ratio) biaxial stretching tests were performed. The dural meninge was found to be most compliant when a circumferentially dominant loading pattern (longitudinal vs. circumferential at a 0.5:1 ratio or physiological biaxial stretch) was used. Additionally, physiological biaxial stretch resulted in the lowest strain energy density in the toe region of stress–strain curves, i.e., physiological deformation ranges, whereas mechanical loading caused abruptly stiffening of the linear region of stress–strain curves even under circumferentially dominant loading. On the other hand, stress relaxation and elastin content of the dural tissue had no effect on stored strain energy density within the range of biaxial stretch tested in this study. These results indicate that physiological biaxial stretching contributes to structural protection of the spinal cord and the spinal dura, which may be attributable to changes in the arrangement of embedded collagen fibers and concomitant mechanical interactions with surrounding tissues.

The paper is devoted to the theoretical analysis of one-dimensional (1D) models of blood flow. The non-Newtonian nature of blood is taken into account. The generalized Newtonian models, in which the dynamic viscosity is dependent only on the shear rate, are considered. The models are constructed by averaging the simplified Navier–Stokes system in cylindrical coordinates.

The 1D models, corresponding to Newtonian, Power Law, Carreau, Carreau–Yasuda, Cross, Simplified Cross, Yeleswarapu, Modified Yeleswarapu, and Quemada models, are compared in this paper. The comparison is performed in order to estimate the influence of non-Newtonian frictional terms, velocity profiles and hematocrit values on the deviations of the solutions from the solution for the Newtonian case. For this purpose, two problems that can be solved analytically are considered: problems for the infinite interval and finite interval with periodic conditions. For the quantitative comparison of solutions, the non-Newtonian factor, which characterizes the relative deviations of solutions from the solution for the Newtonian model, is introduced. The analytical solutions of nonlinear problems are obtained by the perturbation method. As it is demonstrated for both solved problems, the relative deviations increase with the flattening of the velocity profile. For the models that depended on hematocrit, the non-Newtonian factor values increased with the increase in hematocrit value.

The solutions obtained in the paper can be used as a tool for the comparison of different 1D models of blood flow and for the testing of programs that implement numerical algorithms.

This study investigated the predictive ability of the skeletal muscle force model presented by Knodel et al. [Knodel NB, Lawson LB, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part i: Model development,” J Biomech Eng (in press), doi: 10.1115/1.4053568] on the knee joint. It has previously been validated on the ankle joint [Knodel NB, Calvert LB, Bywater EA, Lamia JP, Patel SN, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part ii: Model validation on the ankle joint complex,” Submitted for Publication] and this paper aimed to identify how well it, and the solution process, performed on a more complex articulation. The knee joint’s surrounding musculoskeletal tissue loading was also identified. Ten subjects (five male and five female) performed six exercises targeting the muscles that cross the knee joint. Motion capture, electromyography, and force plate data was collected during the exercises for use in the analysis program written in MATLAB and magnetic resonance images were used to observe subject-specific ligament and tendon data at the knee articulation. OpenSim [Delp, SL, Anderson FC, Arnold AS, Loan P, Habib A, John CT, Guendelman E, Thelen DG, “Opensim: Open-source software to create and analyze dynamic simulations of movement,” IEEE Trans Biomed Eng 54(11):1940–1950, 2007, doi: 10.1109/TBME.2007.901024] was used for scaling a generic lower extremity anatomical model of each subject. Five of the six exercises were used to calculate each muscle’s constant, $K_m$ [Knodel NB, Lawson LB, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part i: Model development,” J Biomech Eng (in press), doi: 10.1115/1.4053568; Knodel NB, Calvert LB, Bywater EA, Lamia JP, Patel SN, Nauman EA, “An emg-based constitutive law for force generation in skeletal muscle-part ii: Model validation on the ankle joint complex,” Submitted for Publication], and the sixth was used as a testing set to identify the model’s predictive ability. Average percent errors ranged from 9.4% to 26.5% and the average across all subjects was 20.6%. The solution process produced physiologically relevant muscle forces and the surrounding tissue loading behaved as expected between the various exercises without approaching respective tensile strength values.