The number of young people getting total hip arthroplasty surgery is on the rise and studies have shown that the average number of perfect health years after such surgery is being reduced to about 9 years; this is because of complications which can lead to the failure of such implants. Consequently, such failures cause the implant not to last as long as required. The uncertainty in design parameters, loading, and even the manufacturing process of femoral stems, makes it important to consider uncertainty quantification and probabilistic modeling approaches instead of the traditional deterministic approach when designing femoral stems. This paper proposes a probabilistic analysis method which considers uncertainties in the design parameters of femoral implants to determine its effect on the implant stiffness. Accordingly, this method can be used to improve the design reliability of femoral stems. A simplified finite element model of a femoral stem was considered and analyzed both deterministically and probabilistically using Monte Carlo simulation. The results showed that uncertainties in design parameters can significantly affect the resulting stiffness of the stem. This paper proposes an approach that can be considered a potential solution for improving, in general, the reliability of hip implants and the predicted stiffness values for the femoral stems so as to better mitigate the stress shielding phenomenon.

Endoscopic radiofrequency ablation has gained interest for treating abdominal tumors. The radiofrequency ablation electrode geometry largely determines the size and shape of the ablation zone. Mismatch between the ablation zone and tumor shapes leads to reoccurrence of the cancer. Recently, work has been published regarding a novel deployable multi-tine electrode for endoscopic radiofrequency ablation. The prior work developed a thermal ablation model to predict the ablation zone surrounding an electrode and a systematic optimization of the electrode shape to treat a specific tumor shape. The purpose of this work is to validate the thermal ablation model through experiments in a tissue phantom that changes color at ablation temperatures. The experiments highlight the importance of thermal tissue damage in finite element modeling. Thermal induced changes in tissue properties, if not accounted for in finite element modeling, can lead to significant overprediction of the expected ablation zone surrounding an electrode.

The increasing complexity of the medical regulatory environment and the inherent complexity of medical devices, especially due to the increased use of connected devices and embedded control software, impose adoption of new methods and tools for the system design, safety and security analyses. In this paper, we propose a method and an associated toolchain to couple model-based system engineering and safety/security analyses at the design phase of medical devices. The method is compliant with ANSI/AAMI/ISO TIR57 safety and security guidance, and compatible with INCOSE Biomedical-Healthcare Model-Based Systems Engineering works. The toolchain is based on a system architecture modelling tool and supports medical device domain specific reference architecture, as well as tools for safety and security risk analyses. The proposed method and toolchain are illustrated by considering a RGB’s TOF-CUFF monitor device analyzed in the scope of the AQUAS project as a medical device use case.

This study is aimed at the design of a novel task-based knee rehabilitation device. The desired trajectories of the knee have been obtained through a vision-based motion capture system. The collected experimental kinematic data has been used as an input to a spatial mechanism synthesis procedure. Parallel mechanisms with single degree-of-freedom (DOF) have been considered to generate the complex 3D motions of the lower leg. An exact workspace synthesis approach is utilized, in which the parameterized forward kinematics equations of each serial chains of the parallel mechanisms are to be converted into implicit equations via elimination. The implicit description of the workspace is made to be a function of the structural parameters of the serial chain, making it easy to relate those parameters to the desired trajectory. The selected mechanism has been verified for the accuracy of its trajectory through CAD modeling and simulations. This design approach reduces alignment and fitting challenges in an exoskeleton as the mechanism does not require alignment of each robotic joint axis with its human counterpart.

There has been an increasing amount of interest in the design and preparation of new biomaterials that can be used in the fabrication of medical devices for artificial prostheses or implant applications. The use of molecular modeling and computational chemistry aids in the design of these materials by calculating different structural properties such as molecular energy, geometry optimization, dipole moments, FTIR, UV-vis, NMR, and others. In this study graphene, polyurethane (PU), polymethylmethacrylate (PMMA) and PU/graphene/PMMA composites have been studied using theoretical calculations. For this work, the AMBER and AM1 simulation methods were used. The results indicate the favorable formation of a cross-linked PU/Graphene composite and adsorption of PMMA.

Fracture toughness is an important mechanical property of materials that describes the failure of material by cracking. Yet, characterizing fracture toughness in soft tissue cutting is still a challenging task as the behavior of the soft tissue may vary under different tissue, cutting and pre-crack conditions [1]. Predicting cutting force has been important to needle biopsy design, surgical planning/training, and other surgical operations. However, in order to obtain accurate predictions, understanding the fracture toughness is crucial. In this study, we present an approach to characterize the fracture toughness directly from cutting experiments of hollow needle cutting soft tissue mimicking materials. Cutting tests are carried out to obtain the dynamic force response of gelatin samples when being cut by non-rotational and rotational hollow needles. The data is used to establish a mixed-mode fracture behavior which is then used to implement a cohesive surface based finite element model. Nearly 1% difference of the axial cutting force between the simulation and experimental results showed that the approach is capable of predicting accurate cutting force in rotational needle biopsy. The approach also has the potential to be used to predict the cutting force in various types of needle biopsy.

3-D culture has been shown to provide cells with a more physiologically authentic environment than traditional 2-D (planar) culture [1, 2]. 3-D cues allow cells to exhibit more realistic functions and behaviors, e.g., adhesion, spreading, migration, metabolic activity, and differentiation. Knowledge of changes in cell morphology, mechanics, and mobility in response to geometrical cues and topological stimuli is important for understanding normal and pathological cell development [3]. Microfabrication provides unique in vitro approaches to recapitulating in vivo conditions due to the ability to precisely control the cellular microenvironment [4, 5]. Microwell arrays have emerged as robust alternatives to traditional 2D cell culture substrates as they are relatively simple and compatible with existing laboratory techniques and instrumentation [6, 7]. In particular, microwells have been adopted as a biomimetic approach to modeling the unique micro-architecture of the epithelial lining of the gastrointestinal (GI) tract [8–10]. The inner (lumen-facing) surface of the intestine has a convoluted topography consisting of finger-like projections (villi) with deep well-like invaginations (crypts) between them. The dimensions of villi and crypts are on the order of hundreds of microns (100–700 μm in height and 50–250 μm in diameter) [11]. While microwells have proven important in the development of physiologically realistic in vitro models of human intestine, existing methods of ensuring their surface is suitable for cell culture are lacking. Sometimes it is desirable to selectively seed cells within microwells and confine or restrict them to the microwells in which they are seeded. Existing methods of patterning microwells for cell attachment either lack selectivity, meaning cells can adhere and migrate anywhere on the microwell array, i.e., inside microwells or outside of them, or necessitate sophisticated techniques such as micro-contact printing, which requires precise alignment and control to selectively pattern the bottoms of microwells for cell attachment [12, 13].

Transcatheter closure of ventricular septal defect (VSD) has become an alternative therapy to open-chest surgery because of simple procedure, less invasion, and high safety [1–2]. The most important occluder device of the therapy is double-disc structure which occludes the VSD, with the discs of the occluder clamp the margin of VSD while the waist of the occluder supports the VSD hole (Figure 1(a)). Commercially available occluders are woven by 72 nitinol wires (Figure1(b)) and then formed by heat treatment. However, the implantation of metal occluders in perimembranous part will result in a substantial risk of complete atrioventricular block (cAVB) averaging 3.5%, because of its close proximity to the conduction system, which passes at the posterior border of defects [3]. To improve the biocompatibility, Huang [4] developed biodegradable VSD occluders (Figure 1(c)) which woven by polydioxanone (PDO) wires and can be fully absorbed within 24 weeks after implantation.

Thermal ablation is rapidly becoming a standard of care for the treatment of atrial fibrillation (AF), a cardiac disorder characterized by irregular heart rhythm and estimated to impact more than 33 million people worldwide [1]. AtriCure is a company that specializes in epicardial ablation for AF and here we describe the development of a numerical model to study the performance of the Isolator ® Synergy™ Clamp bipolar radiofrequency (RF) device. The clamp device features two jaws with embedded electrode pairs, which are used to secure the tissue by clamping across the left atrium (as shown in Figure 1). Energy is applied between the bipolar electrodes at approximately 460 kHz through an impedance-based control algorithm and is additionally duty-cycled between the pairs to further distribute the heating. Patient anatomies vary greatly and measured impedance will depend on atrial wall thickness, epicardial fat, electrode-tissue engagement, and structural variations. Further, tissue conductivity (inversely related to impedance) increases as the tissue is heated, leading to a complicated process, where the heat generation depends on the impedance, which in turn is a strong function of temperature. Energy delivery continues until a phase change in the tissue’s water content occurs, producing a sharp increase in impedance and termination of the ablation. Therefore, since tissue impedance and heating drive the device’s performance, a majority of the effort described here focuses on the validation work done to ensure the model is based on an accurate description of the tissue properties and response. While previous modeling of RF ablation often does include temperature-dependence of tissue properties, the referenced values vary notably and rarely include direct validation of modeling results to benchtop data. Variations in anatomy and fat content can dramatically impact the energy delivery and patient-to-patient treatment efficacy, so an accurate description of the tissue response is critical to understanding the limitations of current energy delivery algorithms and provides an invaluable tool in designing more efficacious ablation devices and algorithms.

As the medical field continues to increase its effectiveness and scope, computational fluid dynamics (CFD) has become essential to understanding flow mechanics cardiovascular systems. Many simulations and experiments have been conducted to confirm the behavior of blood within veins and arteries, under both Newtonian and non-Newtonian conditions. Traditionally, these simulations have been conducted where blood is represented as a homogeneous fluid. However, blood is a heterogeneous fluid mixture, consisting of fluid plasma and solid components of red blood cells (RBC), white blood cells (WBC), and platelets. The effects of the heterogeneity of blood becomes more influential in blood flows through smaller diameter vessels and high velocity flows, as the addition of particles will create variations in flow speed, shear stress, and fluid displacement due to particle-particle and particle-wall collisions [1].

Roboticists have developed a diverse array of powered exoskeletons for human augmentation and rehabilitation over the last few decades. One of the key design objectives is to minimize the discomfort to enhance the user experience. The high inertia and joint misalignment of conventional rigid exoskeletons are two key factors that cause these problems. Different types of control algorithms have been developed to compensate the inertia and render low impedance to the wearers [1–2].

In recent years, outbreaks of highly contagious diseases, like the Ebola virus, have motivated vigorous efforts to screen travelers entering the United States, especially at airports. Screening involves monitoring the body temperature of entering travelers, and blocking entry of those showing a fever, indicating a potential infection. Typically, screening is performed using commercially available non-contact infrared thermometers (NCITs). These thermometers require specific use protocols (e.g., working distances) to provide accurate results, which may not be followed by inspectors reluctant to approach potentially contagious travelers. Furthermore, the NCITs’ accuracy is based on an assumption that the NCIT readings from a forehead will predict the body core temperatures using a simple common one-size-fits-all correction offset. Unfortunately, the temperature detected on the forehead surface by an NCIT may not represent the true body core temperature, due to the changing conditions of the external environment and/or surface conditions of the forehead skin. It is not clear whether the correction factor is able to adjust to the thermal environment, or whether the surface condition of the forehead, including sweat and skin tone, affects the NCIT readings. Before a clinical study is conducted to understand the differences between the forehead temperatures and the body core temperatures, a computational model to simulate temperature distribution inside and on the surface of the body is a cost-effective way to identify factors that influence the temperatures and to study the reasons for their deviations. The objectives of this study were to 1) develop a numerical whole-body model and perform computational heat transfer simulations of different body geometries and 2) perform parametric studies to evaluate the effect of environmental factors, such as air temperature and heat transfer coefficient, on the differences between the forehead temperature and body core temperature. This data can be used to evaluate correction factors or needed to use the measured forehead temperature to predict the body core temperature.

Personal protective equipment (PPE) such as respirators will form the first line of defense in the event of a public health emergency including an airborne pandemic or a bio-terror attack. The two major pathways by which virus-carrying aerosols can reach the human lungs through these PPEs are: a) the intrinsic penetration through porous layers of the PPE and b) the leakage through gaps between the PPE and a person’s face [1, 2]. The contribution from the second pathway can be significantly reduced using fit-testing i.e. by choosing the appropriately sized respirator for a specific face. Unfortunately, in case of an emergency, it would not be possible to fit-test the entire US population. In this scenario, excessive leakage can occur through the gaps. [1]. Hence, it is critical to identify the potential anatomical leak sites (gaps) and quantify the amount of aerosol leakage through surgical respirators for the average US population. At the behest of Office of Counterterrorism and Emerging Threats, the Center for Devices and Radiological Health, US Food and Drug Administration (FDA), has been developing a comprehensive risk assessment model for determining the risk to different populations in case of an “off-label” use of such PPEs, i.e. for public emergency scenarios for which these FDA cleared respirators were not intended to be used. In order to develop the risk assessment model, establishing a correlation between the respirator gaps and aerosol leakage between the face and the respirator is critical. A previous study [3] identified the gaps of N95 surgical respirators for a large population and quantified the aerosol leak using computational fluid dynamics. However, the gap surface area, which is a key parameter required for establishing the gap-aerosol leak correlation, has not been quantified before. In this study, gaps were identified and the gap surface areas were quantified for multiple head-respirator combinations under realistic conditions using imaging coupled with computer-aided design and modeling.

Dramatic news headlines imply that the use of additive manufacturing/3D printing in medicine is a brand new way to save and improve lives. The truth is, it’s not so new. Twenty years ago anatomical models were beginning to be used for planning complicated surgeries. In 2000, hearing aid cases were being 3D-printed and within a few years became industry standard. Medical applications have been a leader in taking 3D printing technology far beyond a product development tool. The combination of using medical imaging data to create patient-matched devices and the ability to manufacture structures difficult to produce with traditional technologies is compelling to an industry always looking for ways to innovate. Surgical uses of 3D printing-centric therapies have a long history beginning with anatomical modeling for bony reconstructive surgery planning[8]. By practicing on a tactile model before surgery surgeons were more prepared and patients received better care. Patient matched implants were a natural extension of this work, leading to truly personalized implants that fit one unique individual[10]. Virtual planning of surgery and guidance using 3D printed, personalized instruments have been applied to many areas of surgery including total joint replacement and craniomaxillofacial reconstruction with great success[9,11]. Further study of the use of models for planning heart and solid organ surgery has lead to increased use in these areas[14]. Finally, hospital-based 3D printing is now of great interest and many institutions are pursuing adding this specialty within individual radiology departments[12,13]. Despite these successful areas of application, widespread use has been fairly slow. Working toward increasing the use of 3D printing in medicine, industry professionals, clinicians, technology developers, and researchers[1] are working together to first identify the challenges and then develop tools and resources to address these challenges.

Biomechanics of optic nerve head (ONH) has attracted increasing attention in recent years due to its association with ganglion cell damage and tissue remodeling resulted vision impairments [1, 2]. The ONH is exposed to both intraocular pressure (IOP) and intracranial pressure (ICP), separated by the lamina cribrosa (LC) which is regarded as the primary site of axonal injury in glaucoma[3]. The elevated IOP was widely acknowledged as a major risk factor for glaucoma. However, a large number of glaucoma patients never have an increase in IOP [4]. In studies that have looked at lumbar puncture (LP) data, patients with open-angle glaucoma were found to have lower ICPs than non-glaucomatous controls[5]. It suggests that higher translaminar pressure difference across the LC rather than IOP alone may have an important role in the pathogenesis of ONH damage. There were few computational models had been established to investigate the ICP’s role on ONH, such as Ethier et al. found elevated ICP could induce decreased strain within LC using finite element model[6]. However, less experimental data are available for delineating the role of ICP on the behaviors of LC. In this work, we present one dataset from LP patients and reconstruct its two-dimensional computational model of the ONH based on the patient’s images to delineate the role of ICP on ONH mechanics. The changes of LC depth, BMO width and papillary height were compared between the simulation and clinical dataset. The maximum principal strain of LC was calculated to reinforce its link with mechanosensitive cells in ONH.

The objective of this paper is to establish a concise structural model of the human musculoskeletal system (HMS) that can be applied to an exercise therapy that treats malfunctions or distortions of the human body. There exist a number of traditional exercise therapy methods in Japan and China, but any systematic approaches for learning, coaching or training are not found to the best of the author’s knowledge. Among such approaches, we deal with an exercise therapy called Somatic Balance Restoring Therapy (SBRT) in which a patient executes a series of non-invasive and painless motions in face-up/down laid posture. Although thousands of results have been piled up in a fixed-format data base, justification for the SBRT has not been provided in bio/mechanical engineering sense. The purpose of modeling is a first step for this holistic approach. For such reasons, the model must be useful and uncomplicated for therapists to identify the problematic areas of the human body with adequate visualization while maintaining a theoretical thoroughness in mechanics or dynamics. To bridge multi-body dynamics and the SBRT, we have utilized a human body model with a collection of joint connected 15 rigid bodies in a topological tree configuration as used for humanoid robot with 80 Degrees-of-Freedom (DOF). In order to achieve the purpose stated above, we have developed a static force/torque balance equation for each body element. In addition, we will describe modeling processes, derivation of static equations, and estimation of parameters/states and verification based on the analysis of the FPS experimental data, and contact forces are parameterized with quantitative values to be given by the Force Plate System (FPS), installed at CARIS at the University of British Columbia (UBC).

A patient-specific computational finite element modeling of the abdominal wall (AW) has been developed to enable biomechanical analysis for customizing and optimizing AW surgical treatments. The methodology consisted on the identification by reverse engineering of patient-specific AW characteristics for physiological pressure settings. The approach combined in vivo experiments and numerical simulations. As a first application, a patient specific model was used to simulate mid-line incision closure technique.

Patient-specific models have been recently applied to investigate a wide range of cardiovascular problems including cardiac mechanics, hemodynamic conditions and structural interaction with devices [1]. The development of dedicated computational tools which combined the advances in the field of image elaboration, finite element (FE) and computational fluid-dynamic (CFD) analyses has greatly supported not only the understanding of human physiology and pathology, but also the improvement of specific interventions taking into account realistic conditions [2, 3]. However, the translation of these technologies into clinical applications is still a major challenge for the engineering modeling community, which has to compromise between numerical accuracy and response time in order to meet the clinical needs [4]. Hence, the validation of in silico against in vivo results is crucial. Finally, if the development of novel tools has recently attracted big investments [5], it has not been similarly easy to dedicate funds and time to test the developed technologies on large numbers of patient cases.

Simulation-based design and certification is fundamentally about making decisions with uncertainty. However, minimizing uncertainty comes at a price — more testing to better define the variability in input parameters, higher fidelity analyses at a finer scale to limit the uncertainty in the physics, etc. Variability in each input parameter does not affect the uncertainty in the system response equally. Nor does every model refinement reduce the uncertainty in the system response. This paper presents a computational methodology that estimates the sensitivity of uncertainty in input variables and the sensitivity of modeling approximations to the final output. In the current age of large multi-disciplinary virtual simulation, this is useful in determining how to minimize overall uncertainty in analytical predictions. In addition, the methodology can be used to optimize for the best use of computational and testing resources to arrive at most robust predictions.

Living tissue engineered heart valves (TEHV) may circumvent ongoing problems in pediatric valve replacements, offering optimum hemodynamic performance and the potential for growth, remodeling, and self-repair [1]. Although a myriad of external stimuli are available in current bioreactors (e.g. oscillatory flows, mechanical conditioning, etc.), there remain significant bioengineering challenges in determining and quantifying parameters that lead to optimal ECM development and structure for the long term goal of engineering TEHVs exhibiting tissue architecture functionality equivalent to native tissue. It has become axiomatic that in vitro mechanical conditioning promotes engineered tissue formation (Figure 1), either in organ-level bioreactors or in tissue-level bioreactors with idealized-geometry TE constructs. However, the underlying mechanisms remain largely unknown. Efforts to date have been largely empirical, and a two-pronged approach involving novel theoretical developments and close-looped designed experiments is necessary to reach a better mechanistic understanding of the cause-effect interplay between MSC proliferation and differentiation, newly synthetized ECM, and tissue formation, in response to the controllable conditions such as scaffold design, oxygen tension, nutrient availability, and mechanical environment during incubation. We thus evaluate the influence of exterior flow oscillatory shear stress and dynamic mechanical conditioning on the proliferative and synthetic behavior of MSCs by employing a novel theoretical framework for TE. We employ mixture theory to describe the evolution of the biochemical constituents of the TE construct and their intertwined biochemical reactions, evolving poroelastic models to evaluate the enhancement of nutrient transport occurring with dynamic mechanical deformations, and computational fluid dynamics (CFD) to assess the exterior flow boundary conditions developed in the flex-stretch-flow (FSF) bioreactor [4–6].