Medical Engineering & Physics最新文献

The reconstruction of extreme long bone defects after malignant tumor resection remains challenging. Short-stem intramedullary implants offer a solution but are often limited by mechanical instability from suboptimal extramedullary fixation. This study assessed how extramedullary low elastic modulus versus high elastic modulus fixation affects osseointegration of these implants by using animal experiments, providing evidence to refine reconstruction techniques. Twenty four rabbits were randomized into LF (low elastic modulus fixation) and HF (high elastic modulus fixation) groups. All received 3D-printed custom short-stem intramedullary implants, differing only in the elastic modulus of the extramedullary fixation component. Weekly x-rays monitored implant stability and complications. Animals were euthanized at 6 and 12 weeks for CT and histological analysis to quantitatively assess callus formation and bone ingrowth into implant threads. X-rays confirmed stable fixation in all animals throughout the observation period. Subsequent CT quantification at 12 weeks demonstrated that the LF group exhibited a significantly greater percentage of callus length relative to total prosthesis length (LF: 55.7 ± 11.4% vs HF: 32.9 ± 8.8%,p< 0.05) and percentage of prosthesis surface wrapped by external callus (LF: 60.8 ± 5.4% vs HF: 31.0 ± 4.7%,p< 0.001). Histological analysis at 12 weeks further confirmed superior osseointegration in the LF group, as evidenced by significantly higher percentage of new bone area in callus relative to predefined thread gap area (LF: 51.8 ± 9.6% vs HF: 7.3 ± 4.1%;p< 0.001), greater percentage of callus-thread edge contact length relative to total thread edge length (LF: 54.1 ± 17.1% vs HF: 7.4 ± 5.7%;p< 0.001), and increased callus penetration depth (LF: 1.637 ± 0.169 mm vs HF: 0.635 ± 0.447 mm;p< 0.05). The Intra-Extramedullary Combined Reconstruction Technique is a reliable method for major bone defect reconstruction. This study provides direct comparative evidence that low elastic modulus extramedullary fixation, unlike its high modulus counterpart, significantly enhances peri-implant callus formation and osseointegration. This represents a novel and important refinement in reconstruction strategy, contributing directly to the potential for long-term implant stability.

Several studies have attempted to develop box simulators for pediatric applications. However, simply reducing the size does not necessarily provide a realistic experience that reflects the characteristics and anatomical variability of pediatric patients. Therefore, there remains an opportunity for innovation in pediatric medical simulation. To address this need, the requirements of surgeons performing pediatric laparoscopic surgery were evaluated to develop a design tailored to their practice. The simulator was based on three-dimensional scans of children and adapted to the structural specifications required for a functional laparoscopic training platform. Training modules focused on suturing tasks were implemented, allowing surgeons from different specialties to evaluate the simulator while performing the procedures. The time taken by each participant to complete each task was recorded. The aspects that received the highest ratings were applicability across specialties (3.8, SD = 0.9), anatomical representation (3.8, SD = 0.8), and port placement (3.9, SD = 0.94). The reduced workspace, combined with a realistic anatomical structure, enabled the practice of laparoscopic skills while addressing the scale and anatomical variability characteristic of pediatric patients. The limited availability of pediatric models, the poor anatomical representation in existing simulators, and the complexity of alternative training methods highlight the need for continued innovation in pediatric medical simulation. The development and validation of our pediatric laparoscopic surgery simulator offer a simple and accessible approach to addressing the shortage of pediatric laparoscopic training systems.

Prosthetic foot quasi-stiffness (QS) and ankle joint QS (AJQS) are factors impacting user biomechanics and comfort. While QS is typically determined by machine-based test methods, AJQS is usually derived from user testing. Despite the use of various methods to assess QS, the relationships and comparability remain unclear. In this study, measurements were performed using five commonly described test methods on three different prosthetic feet (SACH, K2, energy storage and return). A uniaxial test machine was used to record heel and forefoot QS, while a 2D motion tracking system allowed calculation of AJQS. The results revealed significant (p< 0.001) inter-method QS differences of up to 24% (equivalent to approximately two foot categories), as well as differences in AJQS of up to 19% for the heel and 55% for the forefoot. Anterior-posterior forces (APF) varied by as much as 161%, likely due to ambiguities in certain test methods. Overall, each method demonstrated a foot sample-specific non-linear increase in QS, AJQS, and APF. Notably, two of the five methods were unable to differentiate between foot stiffnesses of the prosthetic foot samples.

Spinal surgery procedure is often necessary to remove cancerous tissue within or surrounding the spinal cord and spinal column. This procedure, however, is restricted by the incapability of rigid hand-held drills and cutting tools to reach the target tissue in complex anatomical pathways. Previously, we introduced the surgical robotic platform with a novel concentric connector joint and developed a haptic control system that integrates an active constraint controller into a surgical robot platform. This study introduces the design and experimental testing of a new articulated surgical tool that can navigate around the spinal column using a water jet cutting system for safer removal of tumorous tissue. This innovative approach allows the removal of cancerous tissue on both the anterior and posterior sides of the spinal column without high-speed rotating equipment. A water jet cutting system integrated with an articulated distal tip (ADT) has been developed and successfully tested. A mock surgical procedure on the lower lumbar vertebrae has been used to test the accuracy and performance of the prototype. Results show promise for combining waterjet cutting and an ADT for surgical spinal procedures. The articulated surgical water jet tool removed 82% of mock cancerous tissue compared to 16% of tissue removed by the rigid tool.

Respiratory masks are necessary to protect against airborne contaminants in both medical and industrial environments. However, prolonged use remains uncomfortable and frequently causes pressure sores. This problem usually related to poor fit not only compromises user comfort but also reduces protection effectiveness and compliance with recommendations. The objective is to predict facial deformation and pressure distribution when wearing a mask, based on a limited set of annotated biomechanical data, in order to provide an optimum fit. We designed a semi-supervised graph neural network that represents facial geometries as graph structures. The proposed framework leverages 45 labeled and 120 unlabeled facial datasets employing a variational graph autoencoder constrained by Hertzian contact theory and incorporates an XGBoost-based module for deformation zone classification. Our approach achieves 0.164 mm deformation RMSE (R2=0.9896) and 0.0492 kPa pressure RMSE (R2=0.9517), representing 34.27% improvement over Random Forest, 20.62% improvement over PointNet++ baselines and 10.01% improvement over TPSNET in terms ofR². Five-fold cross-validation confirms robust generalization with minimal overfitting and sub-2-second inference time. This study introduces a real-time personalized respiratory mask model, achieving precise prediction of facial deformation and contact pressure. The approach ensures generalization from limited labeled data, thereby improving comfort, safety, and compliance in medical and industrial applications.

While essential for immobilization and healing following various surgeries, orthopedic casts can contribute to the breakdown of skin integrity due to prolonged pressure, leading to the development of pressure injuries (PIs). Monitoring sub-cast skin integrity allows for early identification and prevention of PIs, reducing the risks of further complications and related healthcare costs. Since temperature is an important indicator of skin integrity, we investigated the correlation between cast-surface and sub-cast temperatures using infrared thermography. Benchtop experiments were carried out using 3D-printed phantoms that simulated skin conditions at different stages of PI, including wound size and exudate. Orthopedic casts of various compositions were applied over the phantoms, which were infrared heated to typical cutaneous temperatures. Thermal images were acquired of the cast surfaces and the corresponding regions of the phantoms sub-cast. Regression analysis revealed that cast-surface temperatures were positively correlated with sub-cast temperatures. The correlation between cast-surface and sub-cast temperatures decreased with additional casting layers. Comparing different casting materials, thermal images of fiberglass cast surfaces retained higher signal-to-noise ratios than those of plaster casts, suggesting more efficient heat transfer. These findings indicate the potential of deriving skin temperature information through casts. Further investigations to determine the spatial and thermal sensitivity of infrared thermography to localized sub-cast skin temperature changes are warranted.

Noninvasive blood glucose detection can avoid pricking fingers to collect blood to obtain blood glucose level (BGL), which can greatly alleviate the pain of diabetic patients. In this paper, a new noninvasive blood glucose detection method using bioimpedance spectroscopy combined with machine learning technology is proposed. Specifically, a data generation method is introduced that adaptively increases the number of missing samples based on the sample density distribution, thereby addressing the problems of small sample sizes and large estimation errors in extreme BGLs. Subsequently, sparse group LASSO with a weight ratio threshold is employed to simultaneously select the frequencies and features with the largest contribution based on prior knowledge of the data structure. Finally, the XGBoost algorithm is used to construct a machine learning regression model. Optuna is used to achieve integrated optimization of all hyperparameters and is evaluated with five-fold cross-validation. Blood glucose data of healthy people and type 2 diabetes patients were collected through oral glucose tolerance test in the laboratory environment. The test results of the model are satisfactory with a mean absolute relative difference of 9.55%. The clinically acceptable zone A + B (A) for the Clarke Error Grid is 99.38% (90.63%). Therefore, our method is expected to be developed into wearable devices to replace traditional invasive methods.

Knee joint distraction is a joint-preserving treatment for younger patients (45-65 years) with tibiofemoral osteoarthritis, aiming to unload cartilage by temporarily separating the femur and tibia. The KneeReviver (KR) is a distraction device specifically developed for this purpose. Despite clinical successes, inter-patient variability in outcomes indicate a limited understanding of its mechanical behavior. This study evaluates the mechanical contribution of KR's bone pins and springs using finite element (FE) modeling. Variations in bone pin length, diameter, and the inclusion of KR's springs were assessed in terms of contact mechanics, initial joint gap creation, and gap narrowing under axial load. Results showed that smaller pin diameters, longer pin lengths, and the inclusion of springs increased cartilage contact pressures, reduced initial joint gaps, and led to earlier joint gap closure. These findings identify critical mechanical factors that may inform future device design, patient-specific configurations, and surgical application strategies.

The development of fully biodegradable cardiac occluders presents unique performance challenges, particularly requiring a balance between initial mechanical strength, appropriate degradation kinetics, and enhanced biocompatibility. Although 4D printing offers promising prospects for manufacturing personalized cardiac occluders, there remains a lack of systematic reviews elucidating how material properties and printing strategies can overcome these performance bottlenecks. This review systematically analyzes research findings on the material-property-processing relationships of materials such as aliphatic polyesters and their application in occluder development. Findings indicate that material modification combined with 4D printing parameter control can achieve nonlinear stress-strain behavior and customized degradation profiles-properties critical for cardiac tissue repair. We contend that integrating material design with 4D printing processes represents a key direction for achieving high-performance, personalized cardiac occluder design and manufacturing.

Peri-prosthetic bone remodelling and bone ingrowth into porous implants are two different evolutionary processes that occur simultaneously post-implantation. Local mechanical stimuli generated in the implant-bone structure play a major role in these processes. Bone remodelling changes the bone density, influencing the local mechanical stimuli. Independent numerical predictions of bone remodelling and bone ingrowth have been reported in the literature. However, the influence of bone remodelling on bone ingrowth has rarely been investigated. The objective of this numerical study was to determine the effect of bone remodelling on bone ingrowth into a lattice porous hip stem using computed-tomography (CT) based model of a femur. In the finite element models, bone material property was assigned based on the CT-grey value. Peak loadings for normal walking and stair climbing were applied. Bone remodelling was simulated in the implanted bone macromodel using a strain-energy-density based phenomenological bone remodelling algorithm. Whereas, bone ingrowth was simulated on four submodels using a mechanoregulatory algorithm. A mapping framework was developed to exchange information between the macroscale model and the submodels. The simulation of bone ingrowth without bone remodelling predicted 72%-91% of bone ingrowth considering all submodels. When bone remodelling was integrated with bone ingrowth simulation, the amount of bone ingrowth was reduced to 66%-81%. Computational model-based validation indicated that the lattice-porous hip stem could achieve an estimated bone in growth of at least 66% under simulated bone remodelling conditions. Consequently, the average Young's moduli of the newly formed tissues (in four submodels) were found to be lower when bone remodelling was integrated with the bone ingrowth simulation. Prediction of bone ingrowth was considerably influenced by the bone remodelling process.