Finite element models in conjunction with adequate constitutive relations are pivotal in several physiological and medical applications related to both native and engineered tissues, allowing to predict the tissue response under various loading states. In order to get reliable results, however, the validation of the constitutive models is crucial. Therefore, the main purpose of this work is to provide an experimental-computational approach to the biomechanical investigation of soft tissues such as the dermis. This is accomplished by implementing and validating three widely adopted hyperelastic constitutive models (the Ogden, the Holzapfel, and the Gasser-Ogden-Holzapfel laws) supposed to be adequate to reproduce human reticular dermis mechanical behavior. Biaxial experimental data have represented the basis for the determination of the respective material parameters identified thanks to the definition of a cost function accounting for the discrepancy between experimental and predicted data. Afterwards, the experimental tests have been reproduced through finite element simulations. Hence, the constitutive laws have been validated comparing experimental and numerical outcomes in terms of displacements of four reference points and stress-strain relations. Hence, an experimental-numerical framework is proposed for the investigation of collagenous tissues, which could become more accurate with larger and independent experimental datasets.
Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals.
Identification of in vivo passive biomechanical properties of healthy human myocardium from regular clinical data is essential for subject-specific modelling of left ventricle (LV). In this work, myocardium was defined by Holzapfel-Ogden constitutive law. Therefore, the objectives of the study were (a) to estimate the ranges of the constitutive parameters for healthy human myocardium using non-invasive routine clinical data, and (b) to investigate the effect of geometry, LV end-diastolic pressure (EDP) and fibre orientations on estimated values. In order to avoid invasive measurements and additional scans, LV cavity volume, measured from routine MRI, and empirical pressure-normalised-volume relation (Klotz-curve) were used as clinical data. Finite element modelling, response surface method and genetic algorithm were used to inversely estimate the constitutive parameters. Due to the ill-posed nature of the inverse optimisation problem, the myocardial properties was extracted by identifying the ranges of the parameters, instead of finding unique values. Additional sensitivity studies were carried out to identify the effect of LV EDP, fibre orientation and geometry on estimated parameters. Although uniqueness of the solution cannot be achieved, the normal ranges of the parameters produced similar mechanical responses within the physiological ranges. These information could be used in future computational studies for designing heart failure treatments.
Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.
Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.
Prediction of sudden cardiac death continues to gain universal attention as a promising approach to saving millions of lives threatened by sudden cardiac death (SCD). This study attempts to promote the literature from mere feature extraction analysis to developing strategies for manipulating the extracted features to target improvement of classification accuracy. To this end, a novel approach to local feature subset selection is applied using meticulous methodologies developed in previous studies of this team for extracting features from non-linear, time-frequency, and classical processes. We are therefore enabled to select features that differ from one another in each 1-min interval before the incident. Using the proposed algorithm, SCD can be predicted 12 min before the onset; thus, more propitious results are achieved. Additionally, through defining a utility function and employing statistical analysis, the alarm threshold has effectively been determined as 83%. Having selected the best combination of features, the two classes are classified using the multilayer perceptron (MLP) classifier. The most effective features would subsequently be discussed considering their prevalence in the rank-based selection. The results indicate the significant capacity of the proposed method for predicting SCD as well as selecting the appropriate processing method at any time before the incident.
Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested.
An understanding of athlete ground reaction forces and moments (GRF/Ms) facilitates the biomechanist’s downstream calculation of net joint forces and moments, and associated injury risk. Historically, force platforms used to collect kinetic data are housed within laboratory settings and are not suitable for field-based installation. Given that Newton’s Second Law clearly describes the relationship between a body’s mass, acceleration, and resultant force, is it possible that marker-based motion capture can represent these parameters sufficiently enough to estimate GRF/Ms, and thereby minimize our reliance on surface embedded force platforms? Specifically, can we successfully use partial least squares (PLS) regression to learn the relationship between motion capture and GRF/Ms data? In total, we analyzed 11 PLS methods and achieved average correlation coefficients of 0.9804 for GRFs and 0.9143 for GRMs. Our results demonstrate the feasibility of predicting accurate GRF/Ms from raw motion capture trajectories in real-time, overcoming what has been a significant barrier to non-invasive collection of such data. In applied biomechanics research, this outcome has the potential to revolutionize athlete performance enhancement and injury prevention.
Bone cells sense mechanical load, which is essential for bone growth and remodeling. In a fracture, this mechanism is compromised. Electromagnetic stimulation has been widely used to assist in bone healing, but the underlying mechanisms are largely unknown. A recent hypothesis suggests that electromagnetic stimulation could influence tissue biomechanics; however, a detailed quantitative understanding of EM-induced biomechanical changes in the bone is unavailable. This paper used a muscle/bone model to study the biomechanics of the bone under EM exposure. Due to the dielectric properties of the muscle/bone interface, a time-varying magnetic field can generate both compressing and shear stresses on the bone surface, where many mechanical sensing cells are available for cellular mechanotransduction. I calculated these stresses and found that the shear stress is significantly greater than the compressing stress. Detailed parametric analysis suggests that both the compressing and shear stresses are dependent on the geometrical and electrical properties of the muscle and the bone. These stresses are also functions of the orientation of the coil and the frequency of the magnetic field. It is speculated that the EM field could apply biomechanical influence to fractured bone, through the fine-tuning of the controllable field parameters.
The level of physical stress rules the adaptative response of peripheral nerves, which is crucial to assess their physiological and pathological states. To this aim, in this work, different computational approaches were presented to model the stress response of in vitro peripheral nerves undergoing longitudinal stretch. More specifically, the effects of geometrical simplifications were studied with respect to the amount of computational time needed to obtain relevant information. Similarly, the variation of compressibility of the peripheral nervous tissue was investigated with respect to the variation of longitudinal stress and transversal stretch variations, and with reference to the computational time needed for simulations. Finally, the effect of small dimensional changes was investigated to better understand whether the variation of time was only due to the amount of nodes or elements. In conclusion, since fast in silico models, able to assess the nerve stress, could be a strategic advantage in case of time constraints or on-line evaluation (e.g., surgical interventions), a synergistic use of these approaches was proposed as a possible strategy to decrease the computational time needed for simulations from minutes to seconds.
Graphical Abstract A synergistic approach involving both symmetry and tuning ofcompressibility allows the computational time to be considerably decreased
Finite element (FE) models are increasingly used to validate experimental data in breast cancer. This research constructed a biomechanical FE model for breast shaped phantoms used to develop and validate a mechanical vibration based screening system. Such models do not currently exist but would enhance development of this screening technology. Three phantoms were modelled: healthy, with 10 and 20 mm inclusions. The overall goal was to create models with enough accuracy to replace experimental phantoms in providing data to optimize diagnostic algorithms for digital image-based elasto-tomography (DIET) screening technologies. FE model results were validating against experimental DIET phantom data for over 4000 collected points on each model and phantom using cross-correlation coefficients between experimental simulated data and direct comparison. Results showed good to strong correlation ranging from 0.7 to 1.0 in all cases with over 90% having a value over 0.9. Magnitudes for each frame of the dynamic response also matched well, indicating that the material properties and geometry were accurate enough to provide this level of correlation. These results justify the use of FE model generated data for in silico diagnostic algorithm development testing. The overall modelling and validation approach is not overly complex, and thus generalizable to similar problems using mechanical properties of silicone phantoms, and might be extensible to human cases with further work.
Graphical abstract Validate that dynamic displacements show that the model can be used in place of phantoms for rapid development of diagnostic algorithms that use surface motion to detect underlying mechanical properties.
Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered environments. Therefore, we propose a reliable method that uses a detection stage and a tracking stage to successfully track mouse. The detection stage detects the surface area of the mouse skin, and the tracking stage implements an extended Kalman filter to estimate the state variables of a nonlinear model. The changes in the overall shape of the mouse are tracked using an oval-shaped tracking model to estimate the parameters for the ellipse. An experiment is conducted to demonstrate the performance of the proposed tracking algorithm using six video images showing various types of movement, and the ground truth values for synthetic images are compared to the values generated by the tracking algorithm. A conventional manual tracking method is also applied to compare across eight experimenters. Furthermore, the effectiveness of the proposed tracking method is also demonstrated by applying the tracking algorithm with actual images of mouse.
In order to solve the problem of the short lifespan of the neural electrode caused by micro motion, this study designed a novel neural electrode based on lumped compliance compliant mechanism to control different modes of micro-motion in a more effective way. According to the mathematical modeling of the novel neural electrode, the equivalent bending stiffness and equivalent tensile (compression) stiffness were calculated. The results of the finite element analysis based on the mathematical modeling revealed that the novel neural electrode showed excellent micro-motion-attenuation capability. The static analysis results showed that the novel design dramatically reduced the maximum displacement of the brain in 51% and the maximum stress in 41% under longitudinal micro-motion environment. It also effectively reduced the 5.1% maximum stress while maintaining the maximum displacement under lateral micro-motion environment. The experimental results based on the tissue injury evaluation system also confirmed that the novel electrode is more effective in micro-motion attenuation than the reference one. In detail, the strain of the brain tissue caused by the implantation of the neural electrode was decreased by 1.26 to 27.84% at the insertion depth of 3 mm, and 0.522 to 17.24% at the insertion depth of 4.5 mm, which has convinced the effectiveness of the design.
Multidirectional defibrillation protocols have shown better efficiency than monodirectional; still, no testing was performed to assess cell lethality. We investigated lethality of multidirectional defibrillator-like shocks on isolated cardiomyocytes. Cells were isolated from adult male Wistar rats and plated into a perfusion chamber. Electrical field stimulation threshold (ET) was obtained, and cells were paced with suprathreshold bipolar electrical field (E) pulses. Either one monodirectional high-intensity electrical field (HEF) pulse aligned at 0° (group Mono0) or 60° (group Mono60) to cell major axis or a multidirectional sequence of three HEF pulses aligned at 0°, 60°, and 120° each was applied. If cell recovered from shock, pacing was resumed, and a higher amplitude HEF, proportional to ET, was applied. The sequence was repeated until cell death. Lethality curves were built by means of survival analysis from sub-lethal and lethal E. Non-linear fit was performed, and E values corresponding to 50% probability of lethality (E50) were compared. Multidirectional groups presented lethality curves similar to Mono0. Mono60 displayed the highest E50. The novel data endorse the idea of multidirectional stimuli being safer because their effects on lethality of individual cells were equal to a single monodirectional stimulus, while their defibrillatory threshold is lower.
Graphical abstract Monodirectional and multidirectional lethality protocol comparison on isolated rat cardiomyocytes. The heart image is a derivative of “3D Heart in zBrush” (https://vimeo.com/65568770) by Laloxl, used under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/legalcode)/image extracted from original video.
The present paper aims at presenting the methodology and first results of a detection system of risk of diabetic macular edema (DME) in fundus images. The system is based on the detection of retinal exudates (Ex), whose presence in the image is clinically used for an early diagnosis of the disease. To do so, the system applies digital image processing algorithms to the retinal image in order to obtain a set of candidate regions to be Ex, which are validated by means of feature extraction and supervised classification techniques. The diagnoses provided by the system on 1058 retinographies of 529 diabetic patients at risk of having DME show that the system can operate at a level of sensitivity comparable to that of ophthalmological specialists: it achieved 0.9000 sensitivity per patient against 0.7733, 0.9133 and 0.9000 of several specialists, where the false negatives were mild clinical cases of the disease. In addition, the level of specificity reached by the system was 0.6939, high enough to screen about 70% of the patients with no evidence of DME. These values show that the system fulfils the requirements for its possible integration into a complete diabetic retinopathy pre-screening tool for the automated management of patients within a screening programme.
In a computer-aided diagnosis (CAD) system, especially for chest radiograph or chest X-ray (CXR) screening, CXR image view information is required. Automatically separating CXR image view, frontal and lateral can ease subsequent CXR screening process, since the techniques may not equally work for both views. We present a novel technique to classify frontal and lateral CXR images, where we introduce angular relational signature through force histogram to extract features and apply three different state-of-the-art classifiers: multi-layer perceptron, random forest, and support vector machine to make a decision. We validated our fully automatic technique on a set of 8100 images hosted by the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH), and achieved an accuracy close to 100%. Our method outperforms the state-of-the-art methods in terms of processing time (less than or close to 2 s for the whole test data) while the accuracies can be compared, and therefore, it justifies its practicality.
This paper presents the design and simulation of a handheld device for people with hand tremor, such as Parkinson’s and essential tremor patients. This device can be used as a pen for smartphones or as a spoon. The designed system includes two links, which are connected to two servomotors, which are mounted in orthogonal directions. To attenuate the effect of hand tremor on the tip of device, PID and computed torque methods are used to actively control the system. These controllers are used to control the rotation of the motors for moving the links in opposite directions of the hand tremor. Performance of the device with mentioned controllers is studied for different applications and finally, the results of both controllers are discussed and compared. Based on the presented results in this study, the designed device is able to suppress the hand tremor up to 75% during eating and 65% during following a spiral pattern.
The aim of this study was to design a system to diagnose chronic stress, based on blunted reactivity of the autonomic nervous system (ANS) to cognitive load (CL). The system concurrently measures CL-induced variations in pupil diameter (PD), heart rate (HR), pulse wave amplitude (PWA), galvanic skin response (GSR), and breathing rate (BR). Measurements were recorded from 58 volunteers whose stress level was identified using the State-Trait Anxiety Inventory. Number-multiplication questions were used as CLs. HR, PWA, GSR, and PD were significantly (p?<?0.05) changed during CL. CL-induced changes in PWA (16.87?±?21.39), GSR (??13.71?±?7.86), and PD (11.56?±?9.85) for non-stressed subjects (n?=?36) were significantly different (p?<?0.05) from those in PWA (2.92?±?12.89), GSR (??6.87?±?9.54), and PD (4.51?±?10.94) for stressed subjects (n?=?22). ROC analysis for PWA, GSR, and PD illustrated their usefulness to identify stressed subjects. By inputting all features to different classification algorithms, up to 91.7% of sensitivity and 89.7% of accuracy to identify stressed subjects were achieved using 10-fold cross-validation. This study was the first to document blunted CL-induced changes in PWA, GSR, and PD in stressed subjects, compared to those in non-stressed subjects. Preliminary results demonstrated the ability of our system to objectively detect chronic stress with good accuracy, suggesting the potential for monitoring stress to prevent dangerous stress-related diseases.
Graphical abstract Chronic stress degrads the autonomic nervous system reaction to cognitive loads. Measurement of reduced changes in physiological signals during asking math questions was useful to identify people with high STAI score (stressed subjects)
Needles are advanced tools commonly used in minimally invasive medical procedures. The accurate manoeuvrability of flexible needles through soft tissues is strongly determined by variations in tissue stiffness, which affects the needle-tissue interaction and thus causes needle deflection. This work presents a variable stiffness mechanism for percutaneous needles capable of compensating for variations in tissue stiffness and undesirable trajectory changes. It is composed of compliant segments and rigid plates alternately connected in series and longitudinally crossed by four cables. The tensioning of the cables allows the omnidirectional steering of the tip and the stiffness tuning of the needle. The mechanism was tested separately under different working conditions, demonstrating a capability to exert up to 3.6 N. Afterwards, the mechanism was integrated into a needle, and the overall device was tested in gelatine phantoms simulating the stiffness of biological tissues. The needle demonstrated the capability to vary deflection (from 11.6 to 4.4 mm) and adapt to the inhomogeneity of the phantoms (from 21 to 80 kPa) depending on the activation of the variable stiffness mechanism.
