The development of magnesium‐based metal matrix composites (MMCs) for biomedical implants is often hindered by the high cost and time associated with conventional trial‐and‐error optimization. In this study, we present an explainable ML framework to accelerate the design and prediction of mechanical performance in magnesium‐based alloys reinforced with HAP and a hybrid system of HAP and GNP. An ensemble of regression models was evaluated, with XGBoost demonstrating superior generalization capability, achieving R 2 values of 0.955 for yield strength, 0.954 for ultimate tensile strength, and 0.939 for % elongation. To ensure model transparency, SHAP was integrated to interpret feature contributions at both global and local levels. The analysis revealed that ECAP and weight percentage of reinforcement are the most influential factors, with nonlinear optimal ranges identified for weight percentage of reinforcement, particle size, and reinforcement. Guided by these insights, a new AZ91‐matrix composite incorporating a hybrid HAP and GNP reinforcement was designed and fabricated via stir casting. Experimental validation demonstrated excellent agreement between model predictions and measured mechanical responses, confirming the high‐fidelity framework. Residual analysis confirmed model robustness and homoscedasticity. This work establishes a transparent, data‐driven framework that bridges predictive modeling with physical interpretability, enabling the rational design of high‐performance, biodegradable implants with reduced reliance on extensive experimentation.

Reconstruction of a traumatic distal femur defect remains a therapeutic challenge. Bone defect implants have been proposed to substitute the bone defect, and their biomechanical performances can be analyzed via a numerical approach. However, the material assumptions for past computational human femur simulations were mainly homogeneous. Thus, this study aimed to design and analyze scaffolds for reconstructing the distal femur defect using a patient-specific finite element modeling technique. A three-dimensional finite element model of the human femur with accurate geometry and material distribution was developed using the finite element method and material mapping technique. An intact femur and a distal femur defect model treated with nine microstructure scaffolds and two solid scaffolds were investigated and compared under a single-leg stance loading. The results showed that the metal solid scaffold design could provide the most stable fixation for reconstructing the distal femur defect. However, the fixation stability was affected by various microstructure designs and pillar diameters. A microstructure scaffold can be designed to satisfy all the biomechanical indexes, opening up future possibilities for more stable reconstructions. A three-dimensional finite element model of the femur with real bone geometry and bone material distribution can be developed, and this patient-specific femur model can be used for studying other femoral fractures or injuries, paving the way for more comprehensive research in the field. Besides, this patient-specific finite element modeling technique can also be applied to developing other human or animal bone models, expanding the scope of biomechanical research.

Four-point bending tests have been used to investigate the flexural strength of orthopedic bone plates. This flexural strength can also be predicted by using the finite element method. However, calculating various metal bone plate designs' flexural strength is time-consuming. This issue would become worse for the design optimization of bone plates. Thus, this study aimed to develop a fast-computing method that integrated finite element analysis and deep learning techniques to evaluate the plate's flexural strength. Three-dimensional parametric finite element models of a metal bone plate under a four-point bending load were developed using ANSYS Workbench. Six design variables of the bone plate were considered. The bone plate's implant stress was calculated and used as learning and verification data for developing the deep-learning model. The convolutional neural network (CNN) was used in this study. The finite element study showed that the implant stress of the bone plates could be evaluated. The CNN-based deep learning model could be successfully developed to predict implant stress quickly. The CNN-based deep learning model could promptly predict the mechanical performances of the bone plate designs. This deep learning model was expected to reduce the cost and time needed to design and optimize bone plates. Integrating finite element analysis with deep learning technique was a feasible and effective tool for predicting the flexural strength of the metal bone plate designs under a four-point bending load.

Objective Spinal fusion surgery is effective for treating various adult spinal deformities. However, spinal fusion surgery is associated with the risk of adjacent segment disease (ASD; 5%–30%), particularly proximal junctional kyphosis (PJK) and proximal junctional failure (PJF). Proximal junctional tethering (PJT) has become a popular technique owing to increasing evidence that it can decrease the rate of PJK or PJF. Methods A literature search was conducted using PubMed, Embase, and Cochrane Library. Twelve eligible studies were identified. These studies were predominantly retrospective in nature and compared the incidence of PJK or PJF in adults undergoing spinal fusion surgery with or without PJT. Risk of bias was assessed using the Newcastle-Ottawa scale. All outcomes were analyzed using R software (ver. 4.4.1). Results We included 8 retrospective cohort studies and 3 propensity-score-matched analyses; these studies comprised 1,424 patients. PJT was associated with a significant decrease in the odds of development of PJK (odds ratio [OR], 0.44; 95% confidence interval [CI], 0.27–0.71) and PJF (OR, 0.36; 95% CI, 0.19–0.69) compared with control. Subgroup analysis results revealed no significant difference in ASD rates between geographical locations, between tethering with and without crosslinks, and between specific tethering techniques. Conclusion PJT significantly reduces the odds of both PJK and PJF in adults undergoing spinal fusion surgery.

Prospective cohort study. To evaluate and compare the performance of artificial intelligence (AI) and 3D printing technologies in preoperative planning for single-level transforaminal lumbar interbody fusion (TLIF). Accurate surgical planning is essential to restore spinal alignment and optimize outcomes in TLIF. While AI and 3D printing have been individually applied in spine surgery, limited studies have compared their predictive accuracy for key parameters such as cage height and postoperative alignment. Twenty patients undergoing single-level TLIF were included. An AI model trained on 311 lumbar x-rays was used to predict cage height and postoperative pelvic incidence minus lumbar lordosis (PI–LL). Separately, 3D-printed surgical planning models were created from CT-based 2D planning. The performance of the AI model during external validation and the performance of the 3D printing model were evaluated by calculating accuracy, root mean square error (RMSE), and mean absolute error (MAE). Due to the small sample size (n=20) and non-normal data distribution, the Wilcoxon signed-rank test was applied to compare the two methods. The AI model achieved an RMSE of 3.28 mm and an MAE of 2.91 mm in cage height prediction. In contrast, the 3D printing approach showed superior accuracy with an RMSE of 0.59 mm and an MAE of 0.25 mm. For postoperative PI-LL prediction, 3D printing also outperformed AI, demonstrating lower RMSE and MAE values (2.62° and 2.02°, respectively). Statistical analysis indicated significant differences in PI-LL prediction between the models ( P =0.037), although no significant difference was found in cage height prediction ( P =0.249). The application of AI and 3D printing significantly enhances the precision of surgical planning in single-level TLIF, improving the selection of appropriate surgical interventions.

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.

The aim of this study was to propose a finite element method based numerical approach for evaluating various hallux valgus treatment strategies. We developed three-dimensional hallux valgus deformity models, with different metatarsal osteotomy methods and Kirschner wire fixation strategies, under two types of standing postures. Ten Kirschner wire fixations were analyzed and compared. The fixation stability, bone stress, implant stress, and contact pressure on the osteotomy surface were calculated as the biomechanical indexes. The results showed that the biomechanical indexes of the osteotomy and Kirschner wire fixations for hallux valgus deformity could be effectively analyzed and fairly evaluated. The distal metatarsal osteotomy method provided better biomechanical indexes compared to the proximal metatarsal osteotomy method. This study proposed a finite element method based numerical approach for evaluating various osteotomy and Kirschner wire fixations for hallux valgus deformity before surgery.

EDITORIAL article Front. Bioeng. Biotechnol., 11 April 2023Sec. Biomechanics Volume 11 - 2023 | https://doi.org/10.3389/fbioe.2023.1178336

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. Here, we developed a fully computer-supported pipeline to predict the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery through preoperative X-ray images. The automated pipeline included two primary stages. First, a deep learning model was used to extract essential features from X-ray images. Second, five machine learning algorithms were trained to identify the optimal models to predict the interbody cage height and postoperative PI-LL. Lasso regression and support vector regression exhibited superior performance for predicting the interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. In addition, the model demonstrated adequate performance for predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, the interbody cage height and postoperative PI-LL can be reliably predicted using artificial intelligence and ML models.

To minimize the stress shielding effect of metallic biomaterials in mimicking bone, the body-centered cubic (bcc) unit cell-based porous CoCrMo alloys with different, designed volume porosities of 20, 40, 60, and 80% were produced via a selective laser melting (SLM) process. A heat treatment process consisting of solution annealing and aging was applied to increase the volume fraction of an ε-hexagonal close-packed (hcp) structure for better mechanical response and stability. In the present study, we investigated the impact of different, designed volume porosities on the compressive mechanical properties in as-built and heat-treated CoCrMo alloys. The elastic modulus and yield strength in both conditions were dramatically decreased with increasing designed volume porosity. The elastic modulus and yield strength of the CoCrMo alloys with a designed volume porosity of 80% exhibited the closest match to those of bone tissue. Different strengthening mechanisms were quantified to determine their contributing roles to the measured yield strength in both conditions. The experimental results of the relative elastic modulus and yield strength were compared to the analytical and simulation modeling analyses. The Gibson–Ashby theoretical model was established to predict the deformation behaviors of the lattice CoCrMo structures.

The human lower extremity is an indispensable system for generating walking and movement. This important system may fail due to joint diseases or bone fractures. This study proposes a human musculoskeletal lower extremity model to calculate its deformation and stress distribution by integrating gait analysis data and finite element analysis. The gait analysis data, which include bone and joint angles, muscle forces, and ground reaction forces, were obtained from a past study and used as the input data in the lower extremity finite element model. The full–field deformation and stress could be calculated and obtained from the musculoskeletal finite element model of the lower extremity with different gait postures. The deformation of the musculoskeletal models satisfactorily mirrored the natural movements of the human lower extremity. Additionally, the high bone stress regions of the musculoskeletal models should be monitored due to the high risk of bone fractures. The human lower extremity model with realistic loading and bounding conditions was successfully developed through the integration of gait analysis and the finite element method. This computational technique could be applied to investigate the effects of various lower extremity postures on the biomechanical mechanism of the human lower extremity.

The incidence of humerus greater tuberosity (GT) fractures is about 20% in patients with proximal humerus fractures. This study aimed to investigate the biomechanical performances of the humerus GT fracture stabilized by a locking plate with rotator cuff function for shoulder rehabilitation activities. A three-dimensional finite element model of the GT-fracture-treated humerus with a single traction force condition was analyzed for abduction, flexion, and horizontal flexion activities and validated by the biomechanical tests. The results showed that the stiffness calculated by the numerical models was closely related to that obtained by the mechanical tests with a correlation coefficient of 0.88. Under realistic rotator cuff muscle loading, the shoulder joint had a larger displacement at the fracture site (1.163 mm), as well as higher bone stress (60.6 MPa), higher plate stress (29.1 MPa), and higher mean screw stress (37.3 MPa) in horizontal flexion rehabilitation activity when compared to that abduction and flexion activities. The horizontal flexion may not be suggested in the early stage of shoulder joint rehabilitation activities. Numerical simulation techniques and experimental designs mimicked clinical treatment plans. These methodologies could be used to evaluate new implant designs and fixation strategies for the shoulder joint.

This paper proposes a dynamic drop weight impact simulation to predict the impact response of 3D printed polymeric sandwich structures using an explicit finite element (FE) approach. The lattice cores of sandwich structures were based on two unit cells, a body-centred cubic (BCC) and an edge-centred cubic (ECC). The deformation and the peak acceleration, referred to as the g-max score, were calculated to quantify their shock absorption characteristic. For the FE results verification, a falling mass impact test was conducted. The FE results were in good agreement with experimental measurements. The results suggested that the strut diameter, strut length, number and orientation, and the apparent material stiffness of the lattice cores had a significant effect on their deformation behavior and shock absorption capability. In addition, the BCC lattice core with a thinner strut diameter and low structural height might lead to poor shock absorption capability caused by structure collapse and border effect, which could be improved by increasing its apparent material stiffness. This dynamic drop impact simulation process could be applied across numerous industries such as footwear, sporting goods, personal protective equipment, packaging, or biomechanical implants.

A numerical approach is one of feasible ways to discover the biomechanics of hallux valgus deformity with various osteotomy and fixation strategies. In the present study, two types of finite element models for analyzing the biomechanical performances of hallux valgus treatment with plate fixations were developed including the single first metatarsal bone model and the musculoskeletal lower extremity model. There are four types of plate fixations that were used to correct the deformity of hallux valgus. The strengths and limitations of both the single first metatarsal bone model and the musculoskeletal lower extremity model were evaluated and discussed. The results revealed that the single metatarsal bone models can be used to quickly predict the biomechanical performances of different hallux valgus treatments. Additionally, the musculoskeletal lower extremity models can be used to predict the biomechanical performances of different hallux valgus treatments under a physiological loading. The plate fixations with the insertion of all locking screws revealed better osteotomy fixation stability and lower risk of the implant failure compared to the other fixations. Additionally, the plate fixations with the insertion of six bone screws had lower risk of the metatarsal bone failure compared to the plate fixations with the insertion of four bone screws. The six holes plate with the insertion of six locking screws was the best treatment among the four treatment strategies. The numerical models and simulation techniques developed in the present study can provide useful information for understanding the biomechanics of hallux valgus treatments.

Pedicle screws might be backed out after screw insertion. Past studies had evaluated the pedicle screws with full insertion and back-out using experimental approaches. Unfortunately, there is rare study to investigate this problem using numerical approaches. Thus, the purpose of this study was to analyze the pullout performance of spinal pedicle screws with fully inserted setting or backed out using finite element method.Twelve types of spinal pedicle screws were developed using SolidWorks. Each screw with the full insertion, backed-out 90 degrees, and backed-out 180 degrees were considered to evaluate their pullout performance using ANSYS Workbench. Additionally, a bone compaction technique was developed and applied in the present study.The results showed that the pullout performance of the conical pedicle screws was significantly reduced compared to that of the cylindrical pedicle screws in situation of screw back-out. Both the screw geometry and bone compaction effect were the key parameters for the evaluation of pullout performance.

Plate or nail fixations have been applied to the repair of clavicle fractures. However, it is quite difficult to fairly evaluate the different clavicle fixation techniques owing to variations in the bone anatomy, bone quality, and fracture pattern. The purpose of this study was to investigate the biomechanical performances of different fixation techniques applied to a clavicle fracture using the finite element method. A simplified single-clavicle model and a complete human upper-body skeleton model were developed in this study. Three types of plate fixations, namely, superior clavicle plate, anterior clavicle plate, and clavicle anatomic spiral fixations, and one nail fixation, a titanium elastic nail fixation, were investigated and compared. The plate fixation techniques have a better fixation stability compared to the nail fixation technique. However, the nail fixation technique shows lower bone stress and can reduce the risk of a peri-implant fracture compared to the plate fixation techniques. Increasing the number of locking screws for the clavicle plate system can reduce the implant stress. Insertion of the bone plate into the anterior site of the clavicle or a multi-plane fixation is recommended to achieve the required biomechanical performance. A plate fixation revealed a relatively better fixation stability, and a nail fixation showed a lower risk of a peri-implant fracture.

Evaluations are vital to quantify the functionalities of athletic footwear, such as the performance of slip resistance, shock absorption, and rebound. Computational technology has progressed to become a promising solution for accelerating product development time and providing customized products in order to keep up with the competitive contemporary footwear market. In this research, the effects of various tread pattern designs on traction performance in a normal gait were analyzed by employing an approach that integrated computational simulation and gait analysis. A state-of-the-art finite element (FE) model of a shoe was developed by digital sculpting technology. A dynamic plantar pressure distribution was automatically applied to interpret individualized subject conditions. The traction performance and real contact area between the shoe and the ground during the gait could be characterized and predicted. The results suggest that the real contact area and the structure of the outsole tread design influence the traction performance of the shoe in dry conditions. This computational process is more efficient than mechanical tests in terms of both cost and time, and it could bring a noticeable benefit to the footwear industry in the early design phases of product development.

Lumbar degenerative disk disease can be treated with posterior lumbar interbody fusion (PLIF) surgery, which mainly applies a rigid fixation system and a solid metal cage. However, previous clinical studies have observed that adjacent segment degenerative (ASD) disease might occur after using those high-stiffness implants. This article uses finite element analysis to investigate the effects of var...