Patient-specific Finite Element Analysis Research Articles

Finite element modeling with patient-specific geometry to assess clinical risks of percutaneous pulmonary valve implantation.

Percutaneous pulmonary valve implantation (PPVI) is a non-surgical treatment for right ventricular outflow tract (RVOT) dysfunction. During PPVI, a stented valve, delivered via catheter, replaces the dysfunctional pulmonary valve. Stent oversizing allows valve anchoring within the RVOT, but overexpansion can intrude on the surrounding structures. Potentially dangerous outcomes include aortic valve insufficiency (AVI) from aortic root (AR) distortion and myocardial ischemia from coronary artery (CA) compression. Currently, risks are evaluated via balloon angioplasty/sizing before stent deployment. Patient-specific finite element (FE) analysis frameworks can improve pre-procedural risk assessment, but current methods require hundreds of hours of high-performance computation. We created a simplified method to simulate the procedure using patient-specific FE models for accurate, efficient pre-procedural PPVI (using balloon expandable valves) risk assessment. The methodology was tested by retrospectively evaluating the clinical outcome of 12 PPVI candidates. Of 12 patients (median age 14.5 years) with dysfunctional RVOT, 7 had native RVOT and 5 had RV-PA conduits. Seven patients had undergone successful RVOT stent/valve placement, three had significant AVI on balloon testing, one had left CA compression, and one had both AVI and left CA compression. A model-calculated change of more than 20% in lumen diameter of the AR or coronary arteries correctly predicted aortic valve sufficiency and/or CA compression in all the patients. Agreement between FE results and clinical outcomes is excellent. Additionally, these models run in 2-6 min on a desktop computer, demonstrating potential use of FE analysis for pre-procedural risk assessment of PPVI in a clinically relevant timeframe.

Patient-specific finite element analysis for assessing hip fracture risk in aging populations

The femur is one of the most important bone in the human body, as it supports the body’s weight and helps with movement. The aging global population presents a significant challenge, leading to an increasing demand for artificial joints, particularly in knee and hip replacements, which are among the most prevalent surgical procedures worldwide. This study focuses on hip fractures, a common consequence of osteoporotic fractures in the elderly population. To accurately predict individual bone properties and assess fracture risk, patient-specific finite element models (FEM) were developed using CT data from healthy male individuals. The study employed ANSYS 2023 R2 software to estimate fracture loads under simulated single stance loading conditions, considering strain-based failure criteria. The FEM bone models underwent meticulous reconstruction, incorporating geometrical and mechanical properties crucial for fracture risk assessment. Results revealed an underestimation of the ultimate bearing capacity of bones, indicating potential fractures even during routine activities. The study explored variations in bone density, failure loads, and density/load ratios among different specimens, emphasizing the complexity of bone strength determination. Discussion of findings highlighted discrepancies between simulation results and previous studies, suggesting the need for optimization in modelling approaches. The strain-based yield criterion proved accurate in predicting fracture initiation but required adjustments for better load predictions. The study underscores the importance of refining density-elasticity relationships, investigating boundary conditions, and optimizing models through in vitro testing for enhanced clinical applicability in assessing hip fracture risk. In conclusion, this research contributes valuable insights into developing patient-specific FEM bone models for clinical hip fracture risk assessment, emphasizing the need for further refinement and optimization for accurate predictions and enhanced clinical utility.

Optimization of S2-alar-iliac screw (S2AI) fixation in adult spine deformity using a comprehensive genetic algorithm and finite element model personalized to patient geometry and bone mechanical properties.

To optimize the biomechanical performance of S2AI screw fixation using a genetic algorithm (GA) and patient-specific finite element analysis integrating bone mechanical properties. Patient-specific pelvic finite element models (FEM), including one normal and one osteoporotic model, were created from bi-planar multi-energy X-rays (BMEXs). The genetic algorithm (GA) optimized screw parameters based on bone mass quality (BM method) while a comparative optimization method maximized the screw corridor radius (GEO method). Biomechanical performance was evaluated through simulations, comparing both methods using pullout and toggle tests. The optimal screw trajectory using the BM method was more lateral and caudal with insertion angles ranging from 49° to 66° (sagittal plane) and 29° to 35° (transverse plane). In comparison, the GEO method had ranges of 44° to 54° and 24° to 30° respectively. Pullout forces (PF) using the BM method ranged from 5 to 18.4kN, which were 2.4 times higher than the GEO method (2.1-7.7kN). Toggle loading generated failure forces between 0.8 and 10.1kN (BM method) and 0.9-2.9kN (GEO method). The bone mass surrounding the screw representing the fitness score and PF of the osteoporotic case were correlated (R2 > 0.8). Our study proposed a patient-specific FEM to optimize the S2AI screw size and trajectory using a robust BM approach with GA. This approach considers surgical constraints and consistently improves fixation performance.

Improved Durability of a Modular Axial Fixator for Stable and Unstable Proximal Femoral Fractures: A Patient-Specific Finite Element Analysis

Abstract Femoral neck fractures, comprising 8–10% of all bodily fractures in the elderly, often necessitate alternatives to extensive surgical interventions. Despite limited research, external fixators are considered promising. This study evaluates the design and durability of a novel modular axial fixator (MAF) for stable and unstable proximal femoral fractures, using numerical method-based engineering analysis. Employing patient-specific CT scan data, three-dimensional (3D) solid modeling, and finite element analysis (FEA), the MAF-bone fixation is examined in eight simulation scenarios under static loading conditions. FEA results show a peak femur head displacement of 7.429 mm in FEA 001, with Schanz screw no. 2 reaching the maximum equivalent stress at 431.060 MPa in FEA-006. Notably, the 7.429-mm displacement improves stability compared to previous studies, yet interfragmentary movement surpasses the 100–200 μm reference range for primary fracture healing, posing challenges to direct healing despite enhanced stability. This study validates the durability of the innovative MAF for femoral neck fractures through engineering simulations. It contributes to understanding MAF durability issues, with implications for improving medical implant design in the industry. Simulation results offer opportunities for optimizing structure and production, enhancing the MAF's design, and ultimately benefiting medical implant manufacturing.

Impact of osteoporosis and Cement-Augmented fusion on adjacent spinal levels Post-Fusion Surgery: Patient-Specific finite element analysis

Cement-augmentation is a technique commonly used during posterior lumbar instrumented fusion (PLIF) to reinforce compromised osteoporotic vertebral bone, minimize the risk of loosening screws, enhance stability, and improve overall surgical outcomes. In this study, we introduce a novel segmented vertebral body regional modeling approach to investigate the effects of osteoporosis and cement-augmented lumbar fusion on disc biomechanics at spinal levels adjacent to the fused vertebrae. Using our previously validated personalized-poroelastic-osteoligamentous FE model of the spine, fusion was simulated at L4-L5, and the biomechanics of adjacent levels were studied for 30 patients (non-osteoporotic patients (N = 15), osteoporotic patients (N = 15)). PLIF models, with and without cement-augmentation, were developed and compared after an 8 h-rest period (200 N), following a 16 h-cyclic compressive loading of 500–1000 N (40 and 20 min, respectively). Movement in different directions (flexion/ extension/ lateral bending/ axial rotation) was simulated using 10Nm moment before and after cyclic loading. The material mapping algorithm was validated by comparing the results of voxel-based and parametric models. The FE cement-augmented models, subject to daily activity loading, demonstrated significant differences in disc height loss and fluid loss as compared to non-cemented models. The calculated axial stress and fiber strain values were also significantly higher for these models. This work demonstrates that although osteoporosis does not significantly alter the time-dependent characteristics of adjacent IVDs post-surgery, cement-augmentation increases the risk of adjacent segment disease (ASD) incidence. A holistic understanding of the trade-offs and long-term complex interplay between structural reinforcement modalities, including cement augmentation, and altered biomechanics warrants further investigation.

FEA-Based Stress-Strain Barometers as Forecasters for Corneal Refractive Power Change in Orthokeratology.

To improve the effectivity of patient-specific finite element analysis (FEA) to predict refractive power change (RPC) in rigid Ortho-K contact lens fitting. Novel eyelid boundary detection is introduced to the FEA model to better model the effects of the lid on lens performance, and stress and strain outcomes are investigated to identify the most effective FEA components to use in modelling. The current study utilises fully anonymised records of 249 eyes, 132 right eyes, and 117 left eyes from subjects aged 14.1 ± 4.0 years on average (range 9 to 38 years), which were selected for secondary analysis processing. A set of custom-built MATLAB codes was built to automate the process from reading Medmont E300 height and distance files to processing and displaying FEA stress and strain outcomes. Measurements from before and after contact lens wear were handled to obtain the corneal surface change in shape and power. Tangential refractive power maps were constructed from which changes in refractive power pre- and post-Ortho-K wear were determined as the refractive power change (RPC). A total of 249 patient-specific FEA with innovative eyelid boundary detection and 3D construction analyses were automatically built and run for every anterior eye and lens combination while the lens was located in its clinically detected position. Maps of four stress components: contact pressure, Mises stress, pressure, and maximum principal stress were created in addition to maximum principal logarithmic strain maps. Stress and strain components were compared to the clinical RPC maps using the two-dimensional (2D) normalised cross-correlation and structural similarity (SSIM) index measure. On the one hand, the maximum principal logarithmic strain recorded the highest moderate 2D cross-correlation area of 8.6 ± 10.3%, and contact pressure recorded the lowest area of 6.6 ± 9%. Mises stress recorded the second highest moderate 2D cross-correlation area with 8.3 ± 10.4%. On the other hand, when the SSIM index was used to compare the areas that were most similar to the clinical RPC, maximum principal stress was the most similar, with an average strong similarity percentage area of 26.5 ± 3.3%, and contact pressure was the least strong similarity area of 10.3 ± 7.3%. Regarding the moderate similarity areas, all components were recorded at around 34.4% similarity area except the contact pressure, which was down to 32.7 ± 5.8%. FEA is an increasingly effective tool in being able to predict the refractive outcome of Ortho-K treatment. Its accuracy depends on identifying which clinical and modelling metrics contribute to the most accurate prediction of RPC with minimal ocular complications. In terms of clinical metrics, age, Intra-ocular pressure (IOP), central corneal thickness (CCT), surface topography, lens decentration and the 3D eyelid effect are all important for effective modelling. In terms of FEA components, maximum principal stress was found to be the best FEA barometer that can be used to predict the performance of Ortho-K lenses. In contrast, contact pressure provided the worst stress performance. In terms of strain, the maximum principal logarithmic strain was an effective strain barometer.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

A high risk of postoperative periprosthetic femoral fracture in Dorr type C femurs: a retrospective cohort study with 10-year follow-up data and a preliminary monochromatic image analysis.

The authors applied Anatomique Benoist Girard II (ABG II) stems for total hip arthroplasty in some Dorr type C femurs as early attempts. Here, the authors compared the long-term follow-up results between ABG II stems and the 'well-performing' Corail stems and their monochromatic images. Among 3214 primary total hip arthroplasty records, 43 short ABG II stems and 67 standard-length Corail stems implanted in Dorr type C femurs were eligible and enrolled in this retrospective cohort study, with a mean follow-up of 10.3 years. Revision rates, Harris hip scores, and radiologic signs were compared. Spectral CT scans from a representative sample were obtained, and monochromatic images were reconstructed. A quantitative method was developed to measure the volume of the gap around stems. Patient-specific finite element analysis was conducted to investigate the strains. The revision rate of ABG II stems was significantly higher than that of Corail stems (21 vs. 3%, P <0.05). In the monochromatic images, fewer spot-weld signs (2.2 vs. 3.4, P <0.05) and wider gaps around stems (1.64cm 3 vs. 0.13cm 3 , P <0.05) were observed on average in the ABG II group. The mean maximum principal strains of the proximal femurs in the ABG II group were close to the yield strains and significantly larger than those in the Corail group (0.0052 vs. 0.0011, P <0.05). There was a high risk of postoperative periprosthetic femoral fracture for ABG II stems in Dorr type C femurs. Monochromatic images provided some insight into the failure mechanism. III.

Effect of intramedullary nail stiffness on load-sharing in tibiotalocalcaneal arthrodesis: A patient-specific finite element study.

Tibiotalocalcaneal (TTC) arthrodesis is a procedure to treat severe ankle and subtalar arthropathy by providing pain free and stable fusion using IM nails. These nails can be manufactured with multiple materials and some feature the ability to dynamize the arthrodesis construct. However, the impact of IM nail material and nail dynamization on load-sharing and in the setting of bone resorption have not been quantified. This work utilized a patient-specific finite element analysis model of TTC arthrodesis to investigate IM nails with differing material moduli and the impact of nail dynamization on load-sharing and intersegmental compression in the setting of bone resorption. Each nail was virtually inserted into a patient-specific model of a hindfoot, which was segmented into the three bones of the TTC complex and assigned material properties based on the densitometry of the bone. Compression, amount of load-sharing, and stress distributions after simulated bone resorption were quantified and compared between the varying IM nails. Simulations revealed that bone segments were only subjected to 17% and 22% of dynamic gait forces in the titanium and carbon fiber nail constructs, whereas the pseudoelastic NiTi nail constructs allowed for 67% of the same. The titanium and carbon fiber nails lost all initial compression in less than 0.13mm of bone resorption, whereas the NiTi nail maintained compression through all simulated values of bone resorption. These data highlight the poor load-sharing of static nail TTC arthrodesis constructs and the ability of a pseudoelastic IM nail construct to maintain intersegmental compression when challenged with bone resorption.

Patient-specific computer modelling to predict anatomical risk factors preventing post transcatheter aortic valve implantation coronary re-access in bicuspid aortic valve; a modelling study

Abstract Background Post Transcatheter aortic valve implantation (TAVI) coronary re-access is predicted to become more common as TAVI expands into lower risk cohorts. The use of a self-expanding valve (SEV) has been recognised as an independent predictor for unsuccessful coronary cannulation. Other predictors include reduced transcatheter heart valve (THV) to coronary distance and small sino-tubular junction. Despite this however a patient cohort remains in which the use of a SEV is preferable due to anatomical complexity or extensive calcification. Purpose Using patient-specific computer modelling, we aimed to assess how the deployment of different sized THVs and at different depths affected the distance from valve to coronary and valve to sino-tubular junction in bicuspid aortic valve cases. Methods In this modelling study we have used pre-procedural, CT-derived, patient-specific finite element analysis computer models to predict the eventual valve result. We studied two anatomical measures for both right and left coronary sinuses. The distance from coronary ostium to nearest structure, either THV or displaced native leaflet – Sinus of Valsalva (SoV) free space. The second distance was from the STJ to THV – STJ free space. THVs were modelled at both high and medium implant positions. THV size was based upon annular dimensions. Where the annular dimensions fell near an anatomical grey-zone, a second size THV was modelled. For each patient and measure, a mean of all distances was calculated, in addition to both minimum and maximum distances. Results The free space SOV left main coronary artery (LMCA) mean was 6.39 mm (4.87-7.91 mm). The difference between maximum and minimum was 0.9461 mm (0.54–1.36 mm), p&amp;lt;0.01. The largest patient-specific difference between minimum and maximum was 2.41 mm, using the same THV size but at different depths. The free space SOV right coronary artery (RCA) mean was 5.73 mm (4.17-7.30 mm). The difference between maximum and minimum was 0.789 mm (0.49-1.09 mm), p&amp;lt;0.01. The STJ free space LMCA mean was 4.3 mm (2.35–6.27 mm), The difference between maximum and minimum was 0.99 mm (0.63-1.35 mm), p&amp;lt;0.01. The STJ free space RCA mean was 3.76 mm (2.2-5.32 mm).The difference between maximum and minimum was 1.18 mm (0.9-1.56 mm), p&amp;lt;0.01. The largest patient-specific difference between minimum and maximum was 2.02 mm, using the same valve size but at different depths. Data presented as mean (95% confidence intervals), n=16 patients/48 models. Conclusions This modelling study has indicated a significant difference in all four measured parameters when different THVs are modelled at different depths. Note some of the largest differences are seen with the same size THV but at a different depth. We would highlight the small mean effect size seen across the results, however in cases where the difference is &amp;gt;2mm, this could have an effect on coronary re-access and warrants further investigation.

Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics

Patient-specific finite element analysis (FEA) holds great promise in advancing the prognosis of cardiovascular diseases by providing detailed biomechanical insights such as high-fidelity stress and deformation on a patient-specific basis. Albeit feasible, FEA that incorporates three-dimensional, complex patient-specific geometry can be time-consuming and unsuitable for time-sensitive clinical applications. To mitigate this challenge, machine learning (ML) models, e.g., deep neural networks (DNNs), have been increasingly utilized as potential alternatives to finite element method (FEM) for biomechanical analysis. So far, efforts have been made in two main directions: (1) learning the input-to-output mapping of traditional FEM solvers and replacing FEM with data-driven ML surrogate models; (2) solving equilibrium equations using physics-informed loss functions of neural networks. While these two existing strategies have shown improved performance in terms of speed or scalability, ML models have not yet provided practical advantages over traditional FEM due to generalization issues. This has led us to the question: instead of abandoning or replacing the traditional FEM framework that can reliably solve biomechanical problems, can we integrate FEM and DNNs to enhance performance?In this study, we propose a synergistic integration of DNNs and FEM to overcome their individual limitations. Using biomechanical analysis of the human aorta as the test bed, we demonstrated two novel integrative strategies in forward and inverse problems. For the forward problem, we developed DNNs with state-of-the-art architectures to predict a nodal displacement field, and this initial DNN solution was then updated by a FEM-based refinement process, yielding a fast and accurate computing framework. For the inverse problem of heterogeneous material parameter identification, our method employs DNN as a regularizer of the spatial distribution of material parameters, aiding the optimizer in locating the optimal solution. In our demonstrative examples, despite that the DNN-only forward models yielded small displacement errors in most test cases; stress errors were considerably large, and for some test cases, the peak stress errors were greater than 50%. Our DNN-FEM integration eliminated these non-negligible errors in DNN-only models and was magnitudes faster than the FEM-only approach. Additionally, compared to FEM-only inverse method with errors greater than 50%, our DNN-FEM inverse approach significantly improved the parameter identification accuracy and reduced the errors to less than 1%.

In silico analysis provides insights for patient-specific annuloplasty in Barlow's disease

ObjectiveTo predict the required mitral annular area reduction in patients with Barlow's disease to obtain a predefined leaflet area index by a novel in silico modeling method. MethodsThree-dimensional echocardiography was used to create patient-specific mitral valve models of 8 patients diagnosed with Barlow's disease and bileaflet prolapse preoperatively. Six patients were also studied postoperatively in a finite element framework, to quantify the optimal coaptation area index. For the patient-specific finite element analyses, realistic papillary muscle and annular motion are incorporated, also for the in silico annuloplasty analyses. The annuloplasty ring size is reduced moderately until the optimal coaptation area index is achieved for each patient. ResultsThe mean mitral annular area at end-diastole was reduced by 58 ± 7% postoperatively (P < .001), resulting in a postoperative coaptation area index of 20 ± 5%. To achieve the same coaptation area index with moderate annular reductions and no leaflet resection the annular reduction was 31 ± 6% (P < .001). ConclusionsIn silico analysis in selected patients diagnosed with Barlow's disease demonstrates that annuloplasty with only moderate annular reduction may be sufficient to achieve optimal coaptation as compared to conventional surgical procedures.

Gluteal activation during squatting reduces acetabular contact pressure in persons with femoroacetabular impingement syndrome: A patient-specific finite element analysis

BackgroundFemoroacetabular impingement syndrome is a motion-related clinical disorder resulting from abnormal hip joint morphology. Mechanical impingement, in which the aspherical femoral head (cam morphology) abuts with the acetabular rim, is created with simultaneous hip flexion, internal rotation, and adduction. Impaired function of the gluteal muscles may be contributory to femoroacetabular impingement syndrome progression. The purpose of this study was to assess the influence of gluteal muscle recruitment on acetabular contact pressure during squatting in persons with cam femoroacetabular impingement syndrome. MethodsEight individuals (4 males, 4 females) with a diagnosis of cam femoroacetabular impingement syndrome underwent CT imaging of the pelvis and proximal femora, and a biomechanical assessment of squatting (kinematics, kinetics, and electromyography). Two maximal depth bodyweight squat conditions were evaluated: 1) non-cued squatting; and 2) cued gluteal activation squatting. Utilizing subject-specific electromyography-driven hip and finite element modeling approaches, hip muscle activation, kinematics, bone-on-bone contact forces, and peak acetabular contact pressure were compared between squat conditions. FindingsModest increases in gluteus maximus (7% MVIC, P < 0.0001) and medius (6% MVIC, P = 0.009) activation were able to reduce hip internal rotation on average 5° (P = 0.024), and in doing so reduced acetabular contact pressure by 32% (P = 0.023). Reductions in acetabular contact pressure occurred despite no change in hip abduction and increased bone-on-bone contact forces occurring in the cued gluteal activation condition. InterpretationOur findings highlight the importance of gluteal activation in minimizing mechanical impingement and provide a foundation for interventions aimed at preventing the development and progression of femoroacetabular impingement syndrome.

Analysis for Predictors of Failure of Orthodontic Mini-implant Using Patient-Specific Finite Element Models.

In this study, we analyzed the clinical factors and mechanical parameters for predicting orthodontic mini-implant (OMI) failure in the mandible, which has different properties from the maxilla. A patient-specific finite element analysis was applied to 32 OMIs (6 failures and 26 successes) implanted between the mandibular second premolars and first molars used for anchorage. The peak stress and strain parameters were calculated for each sample. A logistic regression of the failure (vs. success) of OMIs on the mechanical parameters in the models was conducted. In addition, the influence of clinical factors on the mechanical parameters considered to be related to OMI failure was examined by a regression analysis. The mechanical parameter which best predicts OMI failure in the mandible was found to be a minimum principal strain of between 0.5 to 1.0mm from the OMI surface (R2 = 0.8033). The results indicate the patient's bone density, distance between the OMIs and adjacent root, and vertical implantation angle of the OMIs are potential clinical predictors of OMI failure in the mandible.

Assigning trabecular bone material properties in finite element models simulating the pelvis before and after the development of peri-prosthetic osteolytic lesions

Estimating strain distribution in the acetabulum before and after the development of peri-prosthetic osteolytic lesions secondary to total hip arthroplasty may assist with understanding the pathogenesis of this condition. This could be achieved by performing patient-specific finite element analysis of (1) total hip arthroplasty recipients with developed acetabular osteolytic lesions, and (2) models simulating the patient's pelvis and implant immediately after primary surgery. State of the art patient-specific total hip arthroplasty finite element analysis simulations obtain trabecular bone material properties from Hounsfield units within computed tomography (CT) scans of patients. However, this is not feasible when an implant is already in situ due to metal artefact disruption and, in turn, incorrectly reproduced Hounsfield units. Therefore, alternative methods of assigning trabecular bone material properties within such models were tested and strain results compared. It was found that assigning set material properties throughout the trabecular bone geometry was sufficient for the desired application. Simulating the primary implant and pelvis requires geometric and material based assumptions. Therefore, comparisons were made between strain values obtained from simulated primary models, from state of the art methods using material properties obtained from intact bone within a CT scan, and from models with osteolytic lesions. Strain values found using the finite element models simulating the pelvis before osteolytic lesion developed were considerably closer to those found using state of the art methods than those found for the bone loss models. These models could be used to determine relationships between strain distribution and factors such as bone loss.

Modeling and Predicting Blood Flow Characteristics through Double Stenosed Artery from Computational Fluid Dynamics Simulations using Deep Learning Models

Establishing patient-specific finite element analysis (FEA) models for computational fluid dynamics (CFD) of double stenosed artery models involves time and effort, restricting physicians’ ability to respond quickly in time-critical medical applications. Such issues might be addressed by training deep learning (DL) models to learn and predict blood flow character-istics using a dataset generated by CFD simulations of simplified double stenosed artery models with different configurations. When blood flow patterns are compared through an actual double stenosed artery model, derived from IVUS imaging, it is revealed that the sinusoidal approximation of stenosed neck geometry, which has been widely used in previous research works, fails to effectively represent the effects of a real constriction. As a result, a novel geometric representation of the constricted neck is proposed which, in terms of a generalized simplified model, outperforms the former assumption. The sequential change in artery lumen diameter and flow parameters along the length of the vessel presented opportunities for the use of LSTM and GRU DL models. However, with the small dataset of short lengths of doubly constricted blood arteries, the basic neural network model outperforms the specialized RNNs for most flow properties. LSTM, on the other hand, performs better for predicting flow properties with large fluctuations, such as varying blood pressure over the length of the vessels. Despite having good overall accuracies in training and testing across all the properties for the vessels in the dataset, the GRU model underperforms for an individual vessel flow prediction in all cases. The results also point to the need of individually optimized hyperparameters for each property in any model rather than aiming to achieve overall good performance across all outputs with a single set of hyperparameters.

A new protocol to accurately track long–term orthodontic tooth movement and support patient-specific numerical modeling

Numerical simulation of long-term orthodontic tooth movement based on Finite Element Analysis (FEA) could help clinicians to plan more efficient and mechanically sound treatments. However, most of FEA studies assume idealized loading conditions and lack experimental calibration or validation. The goal of this paper is to propose a novel clinical protocol to accurately track orthodontic tooth displacement in three-dimensions (3D) and provide 3D models that may support FEA. Our protocol uses an initial cone beam computed tomography (CBCT) scan and several intra-oral scans (IOS) to generate 3D models of the maxillary bone and teeth ready for use in FEA. The protocol was applied to monitor the canine retraction of a patient during seven months. A second CBCT scan was performed at the end of the study for validation purposes. In order to ease FEA, a frictionless and statically determinate lingual device for maxillary canine retraction was designed. Numerical simulations were set up using the 3D models provided by our protocol to show the relevance of our proposal. Comparison of numerical and clinical results highlights the suitability of this protocol to support patient-specific FEA.

Enhanced interfragmentary stability and improved clinical prognosis with use of the off-axis screw technique to treat vertical femoral neck fractures in nongeriatric patients

BackgroundThe optimal internal fixation strategy for vertical femoral neck fractures (VFNFs) in nongeriatric patients remains uncertain. Therefore, the purpose of this study was to compare the clinical prognoses and underlying mechanical characteristics of a novel off-axis screw technique with dynamic hip screws (DHSs) and three traditional parallel screws.MethodsThis study included a clinical investigation and a patient-specific finite element analysis (FEA). In the clinical investigation, VFNF patients were grouped by fixation type: (1) use of three parallel screws (G-TRI); (2) augmentation with an off-axis screw (G-ALP); and (3) DHS with an anti-rotational screw (G-DHS). Fixation failures (nonunion, femoral neck shortening (FNS), varus deformation, screw cut-out) and avascular necrosis (AVN) consequent to the three types of fixations were compared. In the FEA, twenty-four fixation models with the three fixation types were created based on the data of eight healthy volunteers. Models were assessed under walking conditions. Stiffness, interfragmentary motion (IFM), and implant stress were evaluated.ResultsIn the clinical investigation, the fixation failure rate was significantly (p < 0.05) lower in G-ALP (18.5%) than in G-DHS (37.5%) and G-TRI (39.3%). No significant difference in AVN was observed among the three fixation groups. In the FEA, stiffness and implant stress in the G-DHS models were significantly (p < 0.05) higher, and the IFM of G-ALP was significantly (p < 0.05) lower among the groups.ConclusionsAmong fixation types for VFNFs, the off-axis screw technique exhibited better interfragmentary stability (lowest IFM) and a lower fixation failure rate (especially FNS). Analyzing interfragmentary stability in biomechanical experiments is more consistent with clinical prognosis than construct stability for VFNFs, suggesting that internal fixations should aim for this outcome.

An application of finite element method in material selection for dental implant crowns.

Materials used for dental crowns show a wide range of variety, and a dentist's choice can depend on several factors such as patient desires, esthetics, tooth factors, etc. One of the most important issues for implant surgery is the primary stability and it should be provided to minimize therisks of screw loosening, failed osseointegration, or nonunion. The current study aims to present the Finite Element Analysis (FEA)-based material selection strategy fora dental crown in terms of reducing the aforementioned risks of dental implants. A virtual surgery mandible model obtained using MIMICS software was transferred to the ANSYS and material candidates determined using CES software were compared using FEA. The results indicated that Zr02+Y2O3 (zirconia) has shown a 12.79% worse performance compared to Au83-88/Pt4-12/Pd4.5-6 alloy in terms of abutment loosening. On the other hand, zirconia is the most promising material for dental crowns in terms of the stability of the bone-implant complex. Therefore, it may show the best overall performance for clinical use. Moreover, as suggested in this study, a better outcome and more accurate predictions can be achieved using a patient-specific FEA approach for the material selection process.

Evaluation of patient tissue selection methods for deriving equivalent density calibration for femoral bone quantitative CT analyses.

Osteoporosis affects an increasing number of people every year and patient specific finite element analysis of the femur has been proposed to identify patients that could benefit from preventative treatment. The aim of this study was to demonstrate, verify, and validate an objective process for selecting tissues for use as the basis of phantomless calibration to enable patient specific finite element analysis derived hip fracture risk prediction. Retrospective reanalysis of patient computed tomography (CT) scans has the potential to yield insights into more accurate prediction of osteoporotic fracture. Bone mineral density (BMD) specific calibration scans are not typically captured during routine clinical practice. Tissue-based BMD calibration can therefore empower the retrospective study of patient CT scans captured during routine clinical practice. Together the method for selecting tissues as the basis for phantomless calibration coupled with the post-processing steps for deriving a calibration equation using the selected tissues provide an estimation of quantitative equivalent density results derived using calibration phantoms. Patient tissues from a retrospective cohort of 211 patients were evaluated. The best phantomless calibration resulted in a femoral strength (FS) [N] bias of 0.069±0.07% over FS derived from inline calibration and a BMD [kg/cm3] bias of 0.038±0.037% over BMD derived from inline calibration. The phantomless calibration slope for the best method presented was within the range of patient specific calibration curves available for comparison and demonstrated a small bias of 0.028±0.054 HU/(mg/cm3), assuming the Mindways Model 3 BMD inline calibration phantom as the gold standard. The presented method of estimating a calibration equation from tissues showed promise for CT-based femoral fracture analyses of retrospective cohorts without readily available calibration data.