A combined neural network and genetic algorithm based approach for optimally designed femoral implant having improved primary stability

Personalized prosthesis design in all-on-4® treatment through deep learning-accelerated structural optimization

Background/purpose

The All-on-4® treatment concept is a dental procedure that utilizes only four dental implants to support a fixed prosthesis, providing full-arch rehabilitation with affordable cost and speedy treatment courses. Although the placement of all-on-4® implants has been researched in the past, little attention was paid to the structural design of the prosthetic framework.

Materials and methods

This research proposed a new approach to optimize the structure of denture framework called BESO-Net, which is a bidirectional evolutionary structural optimization (BESO) based convolutional neural network (CNN). The approach aimed to reduce the use of material for the framework, such as Ti-6Al-4V, while maintaining structural strength. The BESO-Net was designed as a one-dimensional CNN based on Inception V3, trained using finite element analysis (FEA) data from 14,994 design configurations, and evaluated its training performance, generalization capability, and computation efficiency.

Results

The results suggested that BESO-Net accurately predicted the optimal structure of the denture framework in various mandibles with different implant and load settings. The average error was found to be 0.29% for compliance and 11.26% for shape error when compared to the traditional BESO combined with FEA. Additionally, the computational time required for structural optimization was significantly reduced from 6.5 h to 45 s.

Conclusion

The proposed approach demonstrates its applicability in clinical settings to quickly find personalized All-on-4® framework structure that can significantly reduce material consumption while maintaining sufficient stiffness.

Real-time optimization of prosthetic design for complete arch implant-supported treatments using finite element-based machine learning

STATEMENT OF PROBLEM

The complete arch implant-supported treatment concept with 2 angled implants has been widely used for the prosthetic rehabilitation of edentulous patients. While mechanical analysis plays a pivotal role in minimizing suboptimal outcomes or premature failure, it is notably time-consuming. Consequently, clinical treatment planning relies heavily on dentists' subjective judgment and an optimization process is needed.

PURPOSE

The purpose of this study was to develop an optimization process for providing immediate recommendations to support decision-making in configuring complete arch implant-supported prostheses.

MATERIAL AND METHODS

This research was carried out in 2 phases. The first consisted of collecting a dataset from a total of 2800 finite element simulations by randomly configuring 10 implant design variables with 4 types of mandibles. The dataset was used to train an artificial neural network to predict the biomechanical performance of a given complete arch implant-supported prosthesis design configuration. In the second phase, the artificial neural network was used as the objective function predictor in a particle swarm optimization process to enable immediate recommendations for the implant placement. The optimization process was evaluated for accuracy, computing performance, and adaptability for unseen mandibles.

RESULTS

Within the specified design space, the optimization process was able to find an optimal design based on an imported mandible model in 30 seconds. The optimized designs were found to improve peri-implant stress by 11.08 ±6.43%. When verified through finite element analysis, the prediction error was found to be 10.4 ±8.1%. Furthermore, the prediction of the optimal design was highly accurate when tested on 2 unseen mandibles, yielding an error of less than 1.7%.

CONCLUSIONS

The suggested approach can quickly provide an optimal implant configuration for each individual and effectively reduce the average peri-implant stress in the mandible.

Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint

The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.

Advances in Computational Techniques for Bio-Inspired Cellular Materials in the Field of Biomechanics: Current Trends and Prospects

Cellular materials have a wide range of applications, including structural optimization and biomedical applications. Due to their porous topology, which promotes cell adhesion and proliferation, cellular materials are particularly suited for tissue engineering and the development of new structural solutions for biomechanical applications. Furthermore, cellular materials can be effective in adjusting mechanical properties, which is especially important in the design of implants where low stiffness and high strength are required to avoid stress shielding and promote bone growth. The mechanical response of such scaffolds can be improved further by employing functional gradients of the scaffold's porosity and other approaches, including traditional structural optimization frameworks; modified algorithms; bio-inspired phenomena; and artificial intelligence via machine learning (or deep learning). Multiscale tools are also useful in the topological design of said materials. This paper provides a state-of-the-art review of the aforementioned techniques, aiming to identify current and future trends in orthopedic biomechanics research, specifically implant and scaffold design.

On the Various Numerical Techniques for the Optimization of Bone Scaffold

As the application of bone scaffolds becomes more and more widespread, the requirements for the high performance of bone scaffolds are also increasing. The stiffness and porosity of porous structures can be adjusted as needed, making them good candidates for repairing damaged bone tissues. However, the development of porous bone structures is limited by traditional manufacturing methods. Today, the development of additive manufacturing technology has made it very convenient to manufacture bionic porous bone structures as needed. In the present paper, the current state-of-the-art optimization techniques for designing the scaffolds and the settings of different optimization methods are introduced. Additionally, various design methods for bone scaffolds are reviewed. Furthermore, the challenges in designing high performance bone scaffolds and the future developments of bone scaffolds are also presented.

Application of finite element analysis to tissue differentiation and bone remodelling approaches and their use in design optimization of orthopaedic implants: A review

Post-operative bone growth and long-term bone adaptation around the orthopaedic implants are simulated using the mechanoregulation based tissue-differentiation and adaptive bone remodelling algorithms, respectively. The primary objective of these algorithms was to assess biomechanical feasibility and reliability of orthopaedic implants. This article aims to offer a comprehensive review of the developments in mathematical models of tissue-differentiation and bone adaptation and their applications in studies involving design optimization of orthopaedic implants over three decades. Despite the different mechanoregulatory models developed, existing literature confirm that none of the models can be highly regarded or completely disregarded over each other. Not much development in mathematical formulations has been observed from the current state of knowledge due to the lack of in vivo studies involving clinically relevant animal models, which further retarded the development of such models to use in translational research at a fast pace. Future investigations involving artificial intelligence (AI), soft-computing techniques and combined tissue-differentiation and bone-adaptation studies involving animal subjects for model verification are needed to formulate more sophisticated mathematical models to enhance the accuracy of pre-clinical testing of orthopaedic implants.

Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials

Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. STATEMENT OF SIGNIFICANCE: This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine.

Influence of sequential opening/closing of interface gaps and texture density on bone growth over macro-textured implant surfaces using FE based mechanoregulatory algorithm

Intramedullary implant fixation is achieved through a press-fit between the implant and the host bone. A stronger press-fit between the bone and the prosthesis often introduces damage to the bone canal creating micro-gaps. The aim of the present investigation is to study the influences of simultaneous opening/closing of gaps on bone growth over macro-textured implant surfaces. Models based on textures available on CORAIL and SP-CL hip stems have been considered and 3D finite element (FE) analysis has been carried out in conjunction with mechanoregulation based tissue differentiation algorithm. Additionally, using a full-factorial approach, different combinations (between 5 µm to 15 µm) of sliding and gap distances at the bone-implant interface were considered to understand their combined influences on bone growth. All designs show an elevated fibrous tissue formation (10.96% at 5 µm to 29.38% at 40 µm for CORAIL based textured model; 11.45% at 5 µm to 32.25% at 40 µm for SP-CL based textured model) and inhibition of soft cartilaginous tissue (75.64% at 5 µm to 53.94% at 40 µm for CORAIL based model; 76.02% at 5 µm to 53.60% at 40 µm SP-CL based model) at progressively higher levels of normal micromotion, leading to a fragile bone-implant interface. These results highlight the importance of minimizing both sliding and gap distances simultaneously to enhance bone growth and implant stability. Further, results from the studies with differential texture density over CORAIL based implant reveal a non-linear complex relationship between tissue growth and texture density which might be investigated in a machine learning framework.

Qualitative predictions of bone growth over optimally designed macro-textured implant surfaces obtained using NN-GA based machine learning framework

The surface features on implant surface can improve biologic fixation of the implant with the host bone leading to improved secondary (biological) implant stability. Application of finite element (FE) based mechanoregulatory schemes to estimate the amount of bone growth for a wide range of implant surface features is either manually intensive or computationally expensive. This study adopts an integrated approach combining FE, back-propagation neural network (BPNN) and genetic algorithm (GA) based search to evaluate optimum surface macro-textures from three representative implant models so as to enhance bone growth. Initial surface textures chosen for the implant models were based on an earlier investigation. Based on FE predicted dataset, a BPNN was formulated for faster prediction of bone growth. Using the BPNN predicted output, a GA-based search was carried out to maximize bone growth subject to clinically admissible micromotion at the bone-implant interface. The results from FE analysis and bone growth predictions from the BPNN were found to have strong correlation. The optimal osseointegration-maximized-textures (OMTs) obtained were found to offer enhanced biological fixation, as compared to that offered by the textures in the initial models. Results from the present study reveal that certain reduction in the dimension of ribs/grooves promotes bone growth. However, periodic patterns of ribs with higher and lower rib dimensions provide uniform stress environment at the interface thus promoting osseointegration.

Biomechanical design prognosis of two extramedullary fixation devices for subtrochanteric femur fracture: a finite element study

The design rationale of extramedullary fixation for femur fracture has remained a matter of debate in the orthopaedic community. The present work provides a comparative preclinical assessment between two standard fracture fixation techniques: dynamic hip screw (DHS) and proximal femoral locking plate (PFLP), by employing finite element (FE)-based in silico models. The study attempts to evaluate and compare the two implants on following biomechanical behaviours: (1) stress variation on the femur and implant, (2) axial displacement of the fixated femur constructs, (3) postoperative stress shielding and longer term external remodelling of the host bone. We hypothesised that, of the two implants, PFLP has better biomechanical characteristics when used for subtrochanteric femoral fracture (SFF) fixation considering long-term adaptation. A comminuted fracture, simulated as two-part fracture gap of 20 mm, was created in the subtrochanteric region of a femur CAD model. Non-uniform physiological load cases were considered. External bone adaptation was modelled mathematically using stress analysis coupled with a growth model, in which strain energy density (SED) acted as feedback control variable. The computational results predicted lower stress shielding (by ~ 6%) and relatively less cortical thinning beneath the plate for PFLP as compared to DHS. DHS-fixated femur, on the other hand, predicted superior postoperative rigidity. Graphical Abstract FE-based comparative assessment between two extramedullary femur fixation devices-dynamic hip screw (DHS) and proximal femoral locking plate (PFLP).

The influence of loading configurations on numerical evaluation of failure mechanisms in an uncemented femoral prosthesis

The clinical relevance of numerical predictions of failure mechanisms in femoral prosthesis could be impaired due to simplification of musculoskeletal loading. This study investigated the extent to which loading configurations affect the preclinical analysis of an uncemented femoral implant. Patient-specific, CT-scan based FE models of intact and implanted femurs were developed and analysed using three loading configurations, which comprised of load cases representing daily activities. First loading configuration consisted of two load cases, each of walking and stair climbing. The second consisted of more number of load cases for each of these activities. The third included load cases of additional activities of standing up and sitting down. Failure criteria included maximum principal strains, interface debonding, implant-bone relative displacement and adaptive bone remodelling. Simplified loading configurations led to a reduction (100-1500 με) around cortical principal strains. The area prone to interface debonding were observed in the proximo-medial part of implant and was maximum when all activities were considered. This area was reduced by 35%, when simplified loading configurations were chosen. Interfacial area of 88%-96% experienced implant-bone relative displacements below 40 μm; however maximum of 110 μm was observed at the calcar region. Lack of consideration of variety of activities overestimated (30%-50%) bone resorption around the lateral part of the implant; hence, these bone remodelling results were less clinically relevant. Considering a variety daily activities along with an adequate number of load cases for each activity seemed necessary for pre-clinical evaluations of reconstructed femur.

In Silico Optimization of Femoral Fixator Position and Configuration by Parametric CAD Model

Structural analysis, based on the finite element method, and structural optimization, can help surgery planning or decrease the probability of fixator failure during bone healing. Structural optimization implies the creation of many finite element model instances, usually built using a computer-aided design (CAD) model of the bone-fixator assembly. The three most important features of such CAD models are: parameterization, robustness and bidirectional associativity with finite elements (FE) models. Their significance increases with the increase in the complexity of the modeled fixator. The aim of this study was to define an automated procedure for the configuration and placement of fixators used in the treatment of long bone fractures. Automated and robust positioning of the selfdynamisable internal fixator on the femur was achieved and sensitivity analysis of fixator stress on the change of major design parameters was performed. The application of the proposed methodology is considered to be beneficial in the preparation of CAD models for automated structural optimization procedures used in long bone fixation.

Computational intelligence based design of implant for varying bone conditions

The objective is to make the strain deviation before and after implantation adjacent to the femoral implant as close as possible to zero. Genetic algorithm is applied for this optimization of strain deviation, measured in eight separate positions. The concept of composite desirability is introduced in such a way that if the microstrain deviation values for all eight cases are 0, then the composite desirability is 1. Artificial neural network (ANN) models are developed to capture the correlation of the microstrain in femur implants using the data generated through finite element simulation. Then, the ANN model is used as the surrogate model, which in combination with the desirability function serves as the objective function for optimization. The optimum achievable deviation was found to vary with the bone condition. The optimum implant geometry varied for different bone condition, and the findings act as guideline for designing patient-specific implant.

Parametric design of patient-specific fixation plates for distal femur fractures

To facilitate the creation, modification, and optimization of patient-specific plates for distal femur fractures, a novel approach was proposed for the rapid and convenient design of patient-specific plates for patients’ fractured femurs using feature parameterization. First, several femur parameter values were obtained for a specific patient and used to construct a restored surface model of the fractured femur. Next, combined with the particular femur anatomy and the fracture, a parameterized plate with a suitable shape was created automatically based on the parameter maps between the femur and plate. Finally, using finite-element analysis, the Von Mises stresses of the plate under human gait loads were calculated to evaluate the biomechanical performance of the plate, and the plate was optimized for specific patients by recursively adjusting the parameter values. Case results indicate that patient-specific plate models can be created rapidly based on the fractured femur modes of patients and can be optimized efficiently with high-level semantic parameters. Therefore, the proposed approach may be used as a basic tool for the design and modification of patient-specific plates for use in orthopedic operations.