Search Results (51)

Total knee replacement (TKR) is a common procedure for pain relief and restoration of the mobility of the knee joint in patients with severe knee joint problems. Despite this, some patients still suffer from stiffness, instability, or pain caused by soft tissue imbalance, malalignment, or implant-related issues. Previously, surgeons have had to use their experience and visual judgment to balance the knee, which has resulted in variability of outcomes. Smart knee implants are addressing these issues by using sensor technology to provide real-time feedback on joint motion, pressure distribution, and loading forces. This enables more accurate intra-operative adjustment, enhancing implant positioning and soft tissue balance and eliminating post-operative adjustment. These implants also enable post-operative monitoring, simplifying the ability to have more effective individualized rehabilitation programs directed at optimizing patient mobility and minimizing complications. While the patient pool for smart knee implantation remains not commonly documented, it was found in a study that 83.6% of the patients would opt to have the monitoring device implemented, and nearly 90% find reassurance in monitoring their healing indicators. As the number of knee replacements is likely to rise due to aging populations and the rising prevalence of joint disease, smart implants are a welcome development in orthopedics, optimizing long-term success and patient satisfaction. Smart knee implants are built with embedded sensors such as force, motion, temperature, and pressure detectors placed within the implant structure. These sensors provide real-time data during surgery and recovery, allowing earlier detection of complications and supporting tailored rehabilitation. The design aims to improve outcomes through better monitoring and personalized care. Full article

This study presents a methodology for optimizing the design of the fossa component in temporomandibular joint (TMJ) prostheses, particularly in cases requiring replacement due to severe pathology or trauma. Leveraging advancements in 3D printing, the research aims to align prosthetic function with natural jaw movements. A multibody simulation model was used to evaluate different designs based on key performance indicators: range of motion, condylar trajectory accuracy, and contact force magnitudes. Three designs were analyzed: a compact design fossa (CDF) with a spherical condyle, an enhanced design fossa (EDF) with a more anatomically realistic structure, and a simulation-driven design (MEDF) derived from condylar motion patterns. The results indicate that CDF could lead to dislocation at 13° of mouth opening. In contrast, EDF and MEDF safely enabled full opening (20°), closely replicated natural condyle trajectories (with deviations under 2.5 mm in all directions), and reduced contact forces, which can contribute to a longer prosthesis lifespan. MEDF showed the lowest peak contact force (−21% compared to EDF). The study successfully established a framework for evaluating and guiding patient-specific TMJ prosthetic designs, enhancing both functional rehabilitation and mechanical durability by minimizing wear through optimized contact dynamics. Full article

Open-cellular Ti6Al4V lattice structures have found application in porous scaffolds that can match the properties of human bone, which consists of a dense cortical shell and a less-dense cancellous core with an apparent density ranging from 1.3 to 2.1 g/cm³ and 0.1 to 1.3 g/cm³, respectively. The implantation of porous scaffolds is essential for treating large bone defects and must mimic natural bone’s geometric and mechanical behaviour. Functionally graded lattice structures offer spatial variation in mechanical properties, making them suitable for biomedical applications. While the mechanical behaviour of lattice structures is typically evaluated under compression, their flexural properties remain largely underexplored. The aim of this research is to assess the flexural rigidity of a novel lattice material, namely Triply Arranged Octagonal Rings (TAORs), with both uniform and functionally graded architectures, to reproduce the flexural properties of long bones. Titanium alloy scaffolds have been designed with a TAOR cell, whose relative densities range from 10% to 40% with full and hollow sections. Morphological considerations were carried out during the design process to obtain a scaffold geometry which complies with the optimal characteristics required to promote osteointegration. A non-linear finite element (FE) model was developed. Three- and four-point bending tests were simulated, and the results were compared with those of a bone surrogate for long bones. Scaffolds with 10% and 20% relative densities showed flexural rigidity close to that of the bone surrogate and proved to be potential candidates for application in biomedical devices for long bones. Full article

Drug-coated balloons (DCBs) are designed to deliver an anti-proliferative drug to the stenotic vessel to combat restenosis after an angioplasty treatment. However, significant drug loss can occur during device navigation toward the lesion site, thus reducing the delivery efficiency and increasing the off-target drug loss. In this framework, this study aimed to design a novel in vitro setup to estimate the drug loss due to blood flow–coating interaction during tracking. The system consists of a millifluidic chamber, able to host small drug-coated flat patches representative of DCBs, connected at the inlet to a syringe pump able to provide an ad hoc flow and, at the outlet, to a vial collecting the testing fluid with possible drug removed from the specimen. Unlike other studies, the device presented here uniquely evaluates flow-related drug loss from smaller-scale DCB samples, making it a precise, easy-to-use, and efficient assessment tool. In order to define proper boundary conditions for these washing off tests, computational fluid dynamics (CFD) models of a DCB in an idealized vessel were developed to estimate the wall shear stresses (WSSs) experienced in vivo by the device when inserted into leg arteries. From these simulations, different target WSSs were identified as of interest to be replicated in the in vitro setup. A combined analytical–CFD approach was followed to design the testing system and set the flow rates to be imposed to generate the desired WSSs. Finally, a proof-of-concept study was performed by testing eight coated flat specimens and analyzing drug content via high-performance liquid chromatography (HPLC). Results indicated different amounts of drug loss according to the different imposed WSSs and confirmed the suitability of the designed system to assess the washing off resistance of different drug coatings for angioplasty balloons. Full article

Agrivoltaic agrotunnels are currently designed for high-density grow walls that are not amenable to bush berries or root crops. Commercial grow bins provide deeper substrates for produce with more root systems but have high costs per unit growing area. To overcome the economic limitations of grow bins, this study applies the distributed manufacturing open-source design paradigm to develop four designs for low-cost open-source structures. The designs target root vegetables and bush fruit specifically to be adopted by remote communities with limited or no outdoor growing environment to offset the market price for imported fresh produce. The indoor growing designs provide the necessary structure for supporting grow lights and grow bins and enable the transplanted berry plants to flower and produce fruits. They provide a comparable amount (110 L) or more of grow volume from 106 to 192 L. The water reservoir volume for the commercial system (62 L) and grow area (0.5 m³) is surpassed by all new designs that range from 64 to 192 L and 0.51 to 0.76 m³, respectively. These superior properties are possible with material costs for all four designs that save more than 90% of the economic cost of the commercial systems. Full article

The cellular behavior of Voronoi tessellation has generated interest due to its applicability in various fields and its notable structural properties. Controlling factors such as the gradient of the cells, the position of seed points, and the thickness of the arms allows for adjusting rigidity and flexibility according to specific needs. This article analyzes the state of the art of this technique, exploring its modification for applications in engineering and design, complemented with information on natural structural properties. This comprehensive analysis provides a complete overview of Voronoi tessellation and its potential in engineering and design, categorizing methodologies according to selected processing methods and highlighting techniques for altering structural behavior. Additionally, it emphasizes the integration of biomimetic approaches, connecting nature with technology to foster continuous innovation. Finally, this article addresses encountered limitations, offering future perspectives for the cellular technique and highlights the complexity of reproducibility due to reserved or generalized steps, despite the significant diversity in implemented techniques. Full article

Smart dental materials refer to materials used in dentistry with additional functionality to enhance treatment outcomes, which may improve oral health. Smart materials for dental restorations can react to stimuli such as a specific temperature, a different pH, or mechanical stress, repair small cracks or damage by themselves, and interact beneficially with biological surroundings. For example, they might release ions and promote tooth remineralization or have antibacterial properties to prevent bacterial growth. Others can have enhanced mechanical properties like strength and wear resistance to ensure these materials can withstand daily masticatory forces. This review presents our current comprehension of smart dental materials designed for tooth restoration. We focused on what these materials need to be effective, like durability, biocompatibility, and aesthetic requests, besides identifying new ideas for their design. A detailed analysis of the current challenges in formulating these materials, such as the balance between enough ions released with proper physicochemical properties and achieving the desired biological response, was discussed. We also discussed how these cutting-edge technologies are leveraged to overcome existing limitations, creating more dental materials with potential clinical translation. The review also discusses the practical challenges in implementation and the prospects for these materials in dentistry. Full article

Glucose is a simple sugar molecule. The chemical formula of this sugar molecule is C₆H₁₂O₆. This means that the glucose molecule contains six carbon atoms (C), twelve hydrogen atoms (H), and six oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally, after eating or drinking, our bodies break down the sugars in food and use them to obtain energy for our cells. To execute this process, our pancreas produces insulin. Insulin “pulls” sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas cannot produce enough insulin. As a result, the level of glucose in their blood rises. This can lead to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack, damage to blood vessels, etc. In this study, a non-invasive and therefore easily usable method for monitoring blood glucose was developed. With the experiment carried out, it was possible to measure glucose levels continuously, thus eliminating the disadvantages of invasive systems. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in blood; these sensors have a wavelength of 940 nm. The sensor was placed on a small black parallelepiped-shaped box on the tip of the finger and the output of the optical sensor was then connected to a microcontroller at the analogue input. Another sensor used, but only to provide more medical information, was the heartbeat sensor, inserted into an armband (along with the microprocessor). After processing and linear regression analysis, the glucose level was predicted, and data were sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods (commercial products). The hardware consisted of a microcontroller, a near-IR optical sensor, a heartbeat sensor, and a Bluetooth module. Another objective of this experiment using low-cost and low-power hardware was to not carry out complex processing of data from the sensors. Our practical laboratory experiment resulted in an error of 2.86 per cent when compared to a commercial product, with a hardware cost of EUR 8 and a consumption of 50 mA. Full article

Elective mandibular surgical osteotomies are commonly used to correct craniofacial discrepancies. Since the modifications proposed by Obwegeser, Dal Pont, and Hunsuck, no effective variations have been proposed to improve the biomechanical results of these mandibular osteotomies. With technological developments and the use of three-dimensional images from CT scans of patients, much has been done to plan and predict outcomes with greater precision and control. To date, 3D imaging and additive manufacturing technologies have not been used to their full potential to create innovative mandibular osteotomies. The use of 3D digital images obtained from CT scans as DICOM files, which were then converted to STL files, proved to be an efficient method of developing an innovative mandibular ramus beveled osteotomy technique. The new mandibular osteotomy is designed to reduce the likelihood of vasculo-nervous damage to the mandible, reduce the time and ease of surgery, and reduce post-operative complications. The proposed osteotomy does not affect traditional osteotomies. Anatomical structures such as the inferior alveolar nerve and intraoral surgical access were preserved and maintained, respectively. The results obtained from the digital images were validated on an additively manufactured 3D synthetic bone model. Full article

The evolution of 3D printing has ushered in accessibility and cost-effectiveness, spanning various industries including biomedical engineering, education, and microfluidics. In biomedical engineering, it encompasses bioprinting tissues, producing prosthetics, porous metal orthopedic implants, and facilitating educational models. Hybrid Additive Manufacturing approaches and, more specifically, the integration of Fused Deposition Modeling (FDM) with bio-inkjet printing offers the advantages of improved accuracy, structural support, and controlled geometry, yet challenges persist in cell survival, interaction, and nutrient delivery within printed structures. The goal of this study was to develop and present a low-cost way to produce physical phantoms of human organs that could be used for research and training, bridging the gap between the use of highly detailed computational phantoms and real-life clinical applications. To this purpose, this study utilized anonymized clinical Computed Tomography (CT) data to create a liver physical model using the Creality Ender-3 printer. Polylactic Acid (PLA), Polyvinyl Alcohol (PVA), and light-bodied silicone (Polysiloxane) materials were employed for printing the liver including its veins and arteries. In brief, PLA was used to create a mold of a liver to be filled with biocompatible light-bodied silicone. Molds of the veins and arteries were printed using PVA and then inserted in the liver model to create empty channel. In addition, the PVA was then washed out by the final product using warm water. Despite minor imperfections due to the printer’s limitations, the final product imitates the computational model accurately enough. Precision adjustments in the design phase compensated for this variation. The proposed novel low-cost 3D printing methodology successfully produced an anatomically accurate liver physical model, presenting promising applications in medical education, research, and surgical planning. Notably, its implications extend to medical training, personalized medicine, and organ transplantation. The technology’s potential includes injection training for medical professionals, personalized anthropomorphic phantoms for radiation therapy, and the future prospect of creating functional living organs for organ transplantation, albeit requiring significant interdisciplinary collaboration and financial investment. This technique, while showcasing immense potential in biomedical applications, requires further advancements and interdisciplinary cooperation for its optimal utilization in revolutionizing medical science and benefiting patient healthcare. Full article

The reverse engineering (RE) process is often necessary in today’s engineering and medical industries. Expertise in measurement technology, data processing, and CAD modeling is required to ensure accurate reconstruction of an object’s geometry. However, errors are generated at every stage of geometric reconstruction, affecting the dimensional and geometric accuracy of the final 3D-CAD model. In this article, the geometry of reconstructed models was measured using contact and optical methods. The measurement data representing 2D profiles, 3D point clouds, and 2D images acquired in the reconstruction process were saved to a stereolithography (STL) model. The reconstructed models were then subjected to a CAD modeling process, and the accuracy of the parametric modeling was evaluated by comparing the 3D-CAD model to the 3D-STL model. Based on the results, the model used for clamping and positioning parts to perform the machining process and the connecting rod provided the most accurate mapping errors. These models represented deviations within ±0.02 mm and ±0.05 mm. The accuracy of CAD modeling for the turbine blade model and the pelvis part was comparable, presenting deviations within ±0.1 mm. However, the helical gear and the femur models showed the highest deviations of about ±0.2 mm. The procedures presented in the article specify the methods and resolution of the measurement systems and suggest CAD modeling strategies to minimize reconstruction errors. These results can be used as a starting point for further tests to optimize CAD modeling procedures based on the obtained measurement data. Full article

This work presents the study of the thickness vs. stiffness relationship for different materials (PMMA and PEEK) in patient-specific cranial implants, as a criterion for the selection of biomaterials from a mechanical perspective. The geometry of the implant is constructed from the reconstruction of the cranial lesion using image segmentation obtained from computed axial tomography. Different design parameters such as thickness and perforations are considered to obtain displacement distributions under critical loading conditions using finite element analysis. The models consider quasi-static loads with linear elastic materials. The null hypothesis underlying this research asserts that both biomaterials exhibit the minimum mechanical characteristics necessary to withstand direct impact trauma at the implant center, effectively averting critical deformations higher than 2 mm. In this way, the use of PMMA cranioplasties is justified in most cases where a PEEK implant cannot be accessed. Full article

Total knee arthroplasty (TKA) stands out as one of the most widely employed surgical procedures, establishing itself as the preferred method for addressing advanced osteoarthritis of the knee. However, current knee prostheses require refined design solutions. This research work focuses on a computational analysis of both the mechanical behavior of a knee joint implant and the bone remodeling process in the tibia following implantation. This research study delves into how specific design parameters, particularly the stem geometry, impact the prosthesis’s performance. Utilizing a computed tomography scan of a tibia, various TKA configurations were simulated to conduct analyses employing advanced discretization techniques, such as the finite element method (FEM) and the radial point interpolation method (RPIM). The findings reveal that the introduction of the implant leads to a marginal increase in the stress values within the tibia, accompanied by a reduction in the displacement field values. The insertion of the longest tested implant increased the maximum stress from 5.0705 MPa to 6.1584 MPa, leading to a displacement reduction from 0.016 mm to 0.0142 mm. Finally, by combining the FEM with a bone remodeling algorithm, the bone remodeling process of the tibia due to an implant insertion was simulated. Full article

Lung cancer is identified by the uncontrolled proliferation of cells in lung tissues. The timely detection of malignant cells in the lungs, crucial for processes such as oxygen provision and carbon dioxide elimination in the human body, is imperative. The application of deep learning for discerning lymph node involvement in CT scans and histopathological images has garnered widespread attention due to its potential impact on patient diagnosis and treatment. This paper suggests employing DenseNet for lung cancer detection, leveraging its ability to transmit learned features backward through each layer continuously. This characteristic not only reduces model parameters but also enhances the learning of local features, facilitating a better comprehension of the structural complexity and uneven distribution in CT scans and histopathological cancer images. Furthermore, DenseNet accompanied by an attention mechanism (ATT-DenseNet) allows the model to focus on specific parts of an image, giving more weight to relevant regions. Compared to existing algorithms, the ATT-DenseNet demonstrates a remarkable enhancement in accuracy, precision, recall, and the F1-Score. It achieves an average improvement of 20% in accuracy, 19.66% in precision, 24.33% in recall, and 22.33% in the F1-Score across these metrics. The motivation behind the research is to leverage deep learning technologies to enhance the precision and reliability of lung cancer diagnostics, thus addressing the gap in early detection and treatment. This pursuit is driven by the potential of deep learning models, like DenseNet, to provide significant improvements in analyzing complex medical images for better clinical outcomes. Full article