Patient-specific Biomechanical Model Research Articles

Reduction of material groups for vertebral bone finite element simulation: cross comparison of grouping methods

In patient-specific biomechanical modeling, the process of image-to-mesh-material mapping is important, and various strategies have been explored for assigning the number of groups of unique material properties to the mesh. This study aims to cross-compare different grouping strategies to identify the minimum number of unique groups necessary for accurately calculating the fracture load of vertebral bones. We analyzed 12 vertebral specimens by experimentally determining the biomechanical fracture load and acquiring corresponding CT scans. After geometry extraction and meshing, we applied commonly used fixed-value strategies for reducing the number of unique groups, such as Modulus Gaping and Percentual Thresholding. Additionally, we introduced a patient-specific adaptive grouping method based on K-means clustering, which allowed us to maintain a consistent number of groups of unique material properties across the study. A total of 204 simulations were performed, achieving a potential 98% reduction in the number of individual material parameters while maintaining a strong correlation with experimental results when utilizing Percentual Thresholding or Adaptive Clustering, compared to Modulus Gaping. The findings demonstrate the feasibility of significantly reducing simulation complexity while maintaining the accuracy of patient-specific models that strongly correlate with experimental results. This reduction enables efficient processing of patient-specific biomechanical models derived from image data, offering potential benefits for clinicians, particularly in resource-constrained settings.

Biomechanical imaging biomarker during chemoradiotherapy predicts treatment response in head and neck squamous cell carcinoma

Objective. For response-adapted adaptive radiotherapy (R-ART), promising biomarkers are needed to predict post-radiotherapy (post-RT) responses using routine clinical information obtained during RT. In this study, a patient-specific biomechanical model (BM) of the head and neck squamous cell carcinoma (HNSCC) was proposed using the pre-RT maximum standardized uptake value (SUVmax) of 18F-fluorodeoxyglucose (FDG) and tumor structural changes during RT as evaluated using computed tomography (CT). In addition, we evaluated the predictive performance of BM-driven imaging biomarkers for the treatment response of patients with HNSCC who underwent concurrent chemoradiotherapy (CCRT). Approach. Patients with histologically confirmed HNSCC treated with definitive CCRT were enrolled in this study. All patients underwent CT two times as follows: before the start of RT (pre-RT) and 3 weeks after the start of RT (mid-RT). Among these patients, 67 patients who underwent positron emission tomography/CT during the pre-RT period were included in the final analysis. The locoregional control (LC), progression-free survival (PFS), and overall survival (OS) prediction performances of whole tumor stress change (TS) between pre- and mid-RT computed using BM were assessed using univariate, multivariate, and Kaplan–Meier survival curve analyses, respectively. Furthermore, performance was compared with the pre and post-RT SUVmax, tumor volume reduction rate (TVRR) during RT, and other clinical prognostic factors. Main results. For both univariate, multivariate, and survival curve analyses, the significant prognostic factors were as follows (p < 0.05): TS and TVRR for LC; TS and pre-RT FDG-SUVmax for PFS; and TS only for OS. In addition, for 2 year LC, PFS, and OS prediction, TS showed a comparable predictive performance to post-RT FDG-SUVmax. Significance. BM-driven TS is an effective prognostic factor for tumor treatment response after CCRT. The proposed method can be a feasible functional imaging biomarker that can be acquired during RT using only routine clinical data and may provide useful information for decision-making during R-ART.

Automated personalization of biomechanical knee model.

Patient-specific biomechanical models of the knee joint can effectively aid in understanding the reasons for pathologies and improve diagnostic methods and treatment procedures. For deeper research of knee diseases, the development of biomechanical models with appropriate configurations is essential. In this study, we mainly focus on the development of a personalized biomechanical model for the investigation of knee joint pathologies related to patellar motion using automated methods. This study presents a biomechanical model created for patellar motion pathologies research and some techniques for automating the generation of the biomechanical model. To generate geometric models of bones, the U-Net neural network was adapted for 3D input datasets. The method uses the same neural network for segmentation of femur, tibia, patella and fibula. The total size of the train/validation (75/25%) dataset is 18,183 3D volumes of size voxels. The configuration of the biomechanical knee model proposed in the paper includes six degrees of freedom for the tibiofemoral and patellofemoral joints, lateral and medial contact surfaces for femur and tibia, and ligaments, representing, among other things, the medial and lateral stabilizers of the knee cap. The development of the personalized biomechanical model was carried out using the OpenSim software system. The automated model generation was implemented using OpenSim Python scripting commands. The neural network for bones segmentation achieves mean DICE 0.9838. A biomechanical model for realistic simulation of patellar movement within the trochlear groove was proposed. Generation of personalized biomechanical models was automated. In this paper, we have implemented a neural network for the segmentation of 3D CT scans of the knee joint to produce a biomechanical model for the study of knee cap motion pathologies. Most stages of the generation process have been automated and can be used to generate patient-specific models.

Simulation Software to Plan the Treatment of Acetabular Fractures: The Patient-Specific Biomechanical Model.

The objective of this study was to assess the impact of using simulation software for preoperative planning: a patient-specific biomechanical model (PSBM) in acetabular surgery. The secondary objectives were to assess operating time, intraoperative bleeding, and peroperative complications. This is a prospective control study. Level 1 trauma center. Between January 2019 and December 2022, patients with operative acetabular fracture treated by the first author were prospectively enrolled. Patients were divided into 2 groups according to the use or not of PSBM for preoperative planning. When PSBM was used, data were extracted from the preoperative high-resolution computed tomography scans to build a biomechanical model implemented in a custom software [simulation (SIM group)]. When computed tomography scans were not performed in our hospital, PSBM was not feasible (non-SIM group). Radiological results, surgery duration, blood loss, and peroperative complications were recorded. Sixty-six patients were included; 26 in the PSBM group and 40 in the standard group. The 2 groups were comparable regarding fracture patterns and epidemiological data. After simulation, in the SIM group, a poor reduction (>3 mm) was found in 2 of 26 patients (7.7%) versus 11 of 40 patients (27.5%) in the non-SIM group, P = 0.048. The mean operative time was shorter after simulation (110 minutes vs. 155 minutes, P = 0.01), and the mean blood loss was reduced (420 vs. 670 mL, P = 0.01). By reducing the peroperative trials for reduction, PSBM allows better reduction in a shorter operative time and with less blood loss. Level II: prospective study.

Assessing four-dimensional CT stress maps derived from patient-specific biomechanical models of the lung with pulmonary function test data in lung cancer patients.

This study aims to retrospectively compare the stress map of the lung with pulmonary function test (PFT) results in lung cancer patients and to evaluate the potential of the stress map as an imaging biomarker for chronic obstructive pulmonary disease (COPD). 25 lung cancer patients with pre-treatment four-dimensional CT (4DCT) and PFT data were retrospectively analysed. PFT metrics were used to diagnose obstructive lung disease. For each patient, forced expiratory volume in 1 s (FEV1 % predicted) and the ratio of FEV1 and forced vital capacity (FEV1/FVC) were recorded. 4DCT and biomechanical model-deformable image registration (BM-DIR) were used to obtain the lung stress map. The relationship between the mean of the total lung stress and PFT data was evaluated, and the COPD classification grade was also evaluated. The mean values of the total lung stress and FEV1 % predicted showed a significant strong correlation [R = 0.833, (p < 0.001)]. The mean values and FEV1/FVC showed a significant strong correlation [R = 0.805, (p < 0.001)]. For the total lung stress, the area under the curve and the optimal cut-off value were 0.94 and 510.8 Pa for the classification of normal or abnormal lung function, respectively. This study has demonstrated the potential of lung stress maps based on BM-DIR to accurately assess lung function by comparing them with PFT data. The derivation of stress map directly from 4DCT is novel method. The BM-DIR-based lung stress map can provide an accurate assessment of lung function.

Intraoperative estimation of liver boundary conditions from multiple partial surfaces

PurposeA computer-assisted surgical system must provide up-to-date and accurate information of the patient’s anatomy during the procedure to improve clinical outcome. It is therefore essential to consider the tissue deformations, and a patient-specific biomechanical model (PBM) is usually adopted. The predictive capability of the PBM is highly influenced by proper definition of attachments to the surrounding anatomy, which are difficult to estimate preoperatively.MethodsWe propose to predict the location of attachments using a deep neural network fed with multiple partial views of the intraoperative deformed organ surface directly encoded as point clouds. Compared to previous works, providing a sequence of deformed views as input allows the network to consider the temporal evolution of deformations and to handle the intrinsic ambiguity of estimating attachments from a single view.ResultsThe method is applied to computer-assisted hepatic surgery and tested on both a synthetic and in vivo human open-surgery scenario. The network is trained on a patient-specific synthetic dataset in less than 5 h and produces a more accurate intraoperative estimation of attachments than applying the ones generally used in liver surgery (i.e., fixing vena cava or falciform ligament). The obtained results show 26% more accurate predictions than other solution previously proposed.ConclusionsTrained with patient-specific simulated data, the proposed network estimates the attachments in a fast and accurate manner also considering the temporal evolution of the deformations, improving patient-specific intraoperative guidance in computer-assisted surgical systems.

CT Images Segmentation Using a Deep Learning-Based Approach for Preoperative Projection of Human Organ Model Using Augmented Reality Technology

Over the last decades, facing the blooming growth of technological progress, interest in digital devices such as computed tomography (CT) as well as magnetic resource imaging which emerged in the 1970s has continued to grow. Such medical data can be invested in numerous visual recognition applications. In this context, these data may be segmented to generate a precise 3D representation of an organ that may be visualized and manipulated to aid surgeons during surgical interventions. Notably, the segmentation process is performed manually through the use of image processing software. Within this framework, multiple outstanding approaches were elaborated. However, the latter proved to be inefficient and required human intervention to opt for the segmentation area appropriately. Over the last few years, automatic methods which are based on deep learning approaches have outperformed the state-of-the-art segmentation approaches due to the use of the relying on Convolutional Neural Networks. In this paper, a segmentation of preoperative patients CT scans based on deep learning architecture was carried out to determine the target organ’s shape. As a result, the segmented 2D CT images are used to generate the patient-specific biomechanical 3D model. To assess the efficiency and reliability of the proposed approach, the 3DIRCADb dataset was invested. The segmentation results were obtained through the implementation of a U-net architecture with good accuracy.

Biomechanical analysis of rod contouring in posterior spinal instrumentation and fusion for 3D correction of adolescent idiopathic scoliosis.

To biomechanically evaluate 3D corrective forces and deformity correction attributable to key parameters of rod contouring in posterior spinal fusion (PSF) for adolescent idiopathic scoliosis (AIS). Computerised patient-specific biomechanical models of six AIS cases were used to simulate PSF and evaluate the effects of 5.5-mm cobalt-chrome rod contouring angle (concave-convex angles: 30°-15°, 45°-15° and 60°-15°), length (spanning 4 and 7 vertebrae), and apex location (T7, T9). 3D correction and bone-implant forces were computed and analysed. By increasing the concave rod contour from 30° to 60°, thoracic kyphosis (TK) increased from 18° ± 2° (15°-19°) to 24° ± 2° (22°-26°), apical vertebra rotation (AVR) correction increased from 41%(SD8%) to 66%(SD18%) whilst the main thoracic curve (MT) correction decreased from 68%(SD6%) to 56%(SD8%). With a contouring length of 4 vs. 7 vertebrae, the resulting TK, AVR and MT corrections were 22° ± 1° (19°-26°) vs. 19° ± 10° (15°-22°), 57%(SD18%) vs. 50%(SD26%) and 59%(SD1%) vs. 69%(SD35%), respectively. With the rod contouring apex at T7 (vs. T9), AVR corrections were 69% (SD19%) vs. 44% (SD9%), with no significant difference in TK and MT corrections, and with comparatively 67% of screw pull-out forces. Corrective forces were more evenly shared with fixation on 7 vs. 4 vertebrae. Rod contouring of a greater angulation, over a shorter portion of the rod, and more centred at the apex of the main thoracic curve apex improved AVR correction and allowed greater restoration of TK, but resulted in significantly higher screw pull-out forces and came at the expense of less coronal plane correction.

Autonomous Robotic System for Breast Biopsy With Deformation Compensation

Image-guided biopsy is the most common technique for breast cancer diagnosis. Although magnetic resonance imaging (MRI) has the highest sensitivity in breast lesion detection, ultrasound (US) biopsy guidance is generally preferred due to its non-invasiveness and real-time image feedback during the insertion. In this work, we propose an autonomous robotic system for US-guided biopsy of breast lesions identified on pre-operative MRI. After initial MRI to breast registration, the US probe attached to the robotic manipulator compresses the breast tissues until a pre-determined force level is reached. This technique, known as preloading, will allow to minimize lesion displacement during the needle insertion. Our workflow integrates a deformation compensation strategy based on patient-specific biomechanical model to update the US probe orientation keeping the target lesion on the US image plane during compression. By relying on a deformation model, the proposed system does not require lesion visibility on US. Experimental evaluation is performed to assess the performance of the system on a realistic breast phantom with 15 internal lesions, considering different preloading forces. The deformation compensation strategy allows to improve localization accuracy, and as a consequence final probe positioning, for all considered lesions. Median lesion localization error is 3.3 mm, which is lower than the median lesion radius, when applying a preloading of 2 N, which guarantees both minimal needle insertion error and tissue stress.

Automated finite element approach to generate anatomical patient-specific biomechanical models of atherosclerotic arteries from virtual histology-intravascular ultrasound.

Despite advancements in early detection and treatment, atherosclerosis remains the leading cause of death across all cardiovascular diseases (CVD). Biomechanical analysis of atherosclerotic lesions has the potential to reveal biomechanically instable or rupture-prone regions. Treatment decisions rarely consider the biomechanics of the stenosed lesion due in-part to difficulties in obtaining this information in a clinical setting. Previous 3D FEA approaches have incompletely incorporated the complex curvature of arterial geometry, material heterogeneity, and use of patient-specific data. To address these limitations and clinical need, herein we present a user-friendly fully automated program to reconstruct and simulate the wall mechanics of patient-specific atherosclerotic coronary arteries. The program enables 3D reconstruction from patient-specific data with heterogenous tissue assignment and complex arterial curvature. Eleven arteries with coronary artery disease (CAD) underwent baseline and 6-month follow-up angiographic and virtual histology-intravascular ultrasound (VH-IVUS) imaging. VH-IVUS images were processed to remove background noise, extract VH plaque material data, and luminal and outer contours. Angiography data was used to orient the artery profiles along the 3D centerlines. The resulting surface mesh is then resampled for uniformity and tetrahedralized to generate the volumetric mesh using TetGen. A mesh convergence study revealed edge lengths between 0.04 mm and 0.2 mm produced constituent volumes that were largely unchanged, hence, to save computational resources, a value of 0.2 mm was used throughout. Materials are assigned and finite element analysis (FEA) is then performed to determine stresses and strains across the artery wall. In a representative artery, the highest average effective stress was in calcium elements with 235 kPa while necrotic elements had the lowest average stress, reaching as low as 0.79 kPa. After applying nodal smoothening, the maximum effective stress across 11 arteries remained below 288 kPa, implying biomechanically stable plaques. Indeed, all atherosclerotic plaques remained unruptured at the 6-month longitudinal follow up diagnosis. These results suggest our automated analysis may facilitate assessment of atherosclerotic plaque stability.

Influence of spinal lordosis correction location on proximal junctional failure: a biomechanical study.

Assessment of sagittal lordosis distribution on mechanical proximal junctional failure-related risks through computer-based biomechanical models. To biomechanically assess how lordosis distribution influences radiographical and biomechanical indices related to Proximal Junctional Failure (PJF). The "optimal" patient-specific targets to restore the sagittal balance in posterior spinal fusion are still not known. Among these, the effect of the lumbar lordosis correction strategy on complications such as PJF remain uncertain. In this computational biomechanical study, five adult spinal deformity patients who underwent posterior spinal fixation were retrospectively reviewed. Their surgery, first erect posture and flexion movement were simulated with a patient-specific multibody model. Three pedicle subtraction osteotomy (PSO) levels (L3, L4, and L5) were simulated, with consistent global lordosis for a given patient and pelvic tilt adjusted accordingly to the actual surgery. Computed loads on the anterior spine and instrumentation were analyzed and compared using Kruskal-Wallis statistical tests and Spearman correlations. In these models, no significant correlations were found between the lordosis distribution index (LDI), PSO level and biomechanical PJF-related indices. However, increasing the sagittal vertical axis (SVA) and thoracolumbar junction angle (TLJ) and decreasing the sacral slope (SS) increased the bending moment sustained by the rods at the proximal instrumented level (r = 0.52, 0.57, -0.56, respectively, p < 0.05). There was a negative correlation between SS and the bending moment held by the adjacent proximal segment (r = -0.71, p < 0.05). Based on these biomechanical simulations, there was no correlation between the lordosis distribution and PJF-associated biomechanical factors. However, increasing SS and flattening the TLJ, as postural adjustment strategies required by a more distal PSO, did decrease such PJF-related factors. Sagittal restoration and PJF risks remain multifactorial, and the use of patient-specific biomechanical models may help to better understand the complex interrelated mechanisms.

Biomechanical Morphing for Personalized Fitting of Scoliotic Torso Skeleton Models.

The use of patient-specific biomechanical models offers many opportunities in the treatment of adolescent idiopathic scoliosis, such as the design of personalized braces. The first step in the development of these patient-specific models is to fit the geometry of the torso skeleton to the patient’s anatomy. However, existing methods rely on high-quality imaging data. The exposure to radiation of these methods limits their applicability for regular monitoring of patients. We present a method to fit personalized models of the torso skeleton that takes as input biplanar low-dose radiographs. The method morphs a template to fit annotated points on visible portions of the spine, and it relies on a default biomechanical model of the torso for regularization and robust fitting of hardly visible parts of the torso skeleton, such as the rib cage. The proposed method provides an accurate and robust solution to obtain personalized models of the torso skeleton, which can be adopted as part of regular management of scoliosis patients. We have evaluated the method on ten young patients who participated in our study. We have analyzed and compared clinical metrics on the spine and the full torso skeleton, and we have found that the accuracy of the method is at least comparable to other methods that require more demanding imaging methods, while it offers superior robustness to artifacts such as interpenetration of ribs. Normal-dose X-rays were available for one of the patients, and for the other nine we acquired low-dose X-rays, allowing us to validate that the accuracy of the method persisted under less invasive imaging modalities.

Computational model predicts risk of spinal screw loosening in patients

PurposePedicle screw loosening is a frequent complication in lumbar spine fixation, most commonly among patients with poor bone quality. Determining patients at high risk for insufficient implant stability would allow clinicians to adapt the treatment accordingly. The aim of this study was to develop a computational model for quantitative and reliable assessment of the risk of screw loosening.MethodsA cohort of patient vertebrae with diagnosed screw loosening was juxtaposed to a control group with stable fusion. Imaging data from the two cohorts were used to generate patient-specific biomechanical models of lumbar instrumented vertebral bodies. Single-level finite element models loading the screw in axial or caudo-cranial direction were generated. Further, multi-level models incorporating individualized joint loading were created.ResultsThe simulation results indicate that there is no association between screw pull-out strength and the manifestation of implant loosening (p = 0.8). For patient models incorporating multiple instrumented vertebrae, CT-values and stress in the bone were significantly different between loose screws and non-loose screws (p = 0.017 and p = 0.029, for CT-values and stress, respectively). However, very high distinction (p = 0.001) and predictability (R2Pseudo = 0.358, AUC = 0.85) were achieved when considering the relationship between local bone strength and the predicted stress (loading factor). Screws surrounded by bone with a loading factor higher than 25% were likely to be loose, while the chances of screw loosening were close to 0 with a loading factor below 15%.ConclusionThe use of a biomechanics-based score for risk assessment of implant fixation failure might represent a paradigm shift in addressing screw loosening after spondylodesis surgery.

Automatic framework for patient-specific modelling of tumour resection-induced brain shift

Our motivation is to enable non-biomechanical engineering specialists to use sophisticated biomechanical models in the clinic to predict tumour resection-induced brain shift, and subsequently know the location of the residual tumour and its boundary. To achieve this goal, we developed a framework for automatically generating and solving patient-specific biomechanical models of the brain. This framework automatically determines patient-specific brain geometry from MRI data, generates patient-specific computational grid, assigns material properties, defines boundary conditions, applies external loads to the anatomical structures, and solves differential equations of nonlinear elasticity using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm. We demonstrated the effectiveness and appropriateness of our framework on real clinical cases of tumour resection-induced brain shift.

3D Patient-Specific Biomechanical Model of the Tongue for the Management of Tongue Tumors: Conceptualization to Reality.

Increasing use of tobacco by the younger generation has resulted in an increase in oral cavity tumors. Surgical treatment is radical and results in severe functional morbidity. Using computer-aided designing technology, surgical and rehabilitative planning can be better. We present here our concept of a patient-specific biomechanical 3D model of the tongue and its clinical utility in the management of tongue tumors. Using fused deposition modeling, the 3D model of the tongue was printed which easily differentiates the tumor and the uninvolved tongue by printing in two different colors. The 3D tongue model was used by the surgical and rehabilitation teams to frame the treatment and plan the rehabilitation taking into account the patient preferences and needs. The model was used in two patients with operable squamous cell carcinoma (SCC) of the tongue, and the utility of the model in margin planning, surgical defect assessment, and its aid in the reconstruction and rehabilitation was assessed. Two patients with Stage III SCC of the tongue underwent the surgery based on the plan evolved from the 3D model. All the surgical margins assessed by the frozen section analysis were clear. The model helped in addressing the discordance between patient expectations and surgical outcomes. We found that the model aided the reconstructive surgeon in planning the flap harvest based on the pre-operative defect assessment, which, in turn, translated into better rehabilitative outcomes. 3D biomechanical tongue model is a novel concept and aids in improving the overall treatment outcomes. The realistic 3D reconstructed image model helps the oncologist in planning the resection, enables the reconstructive surgery to more precisely predict the defect volume, and lastly the rehabilitative team in developing better rehabilitation strategies.

Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor

Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems.

Musculoskeletal biomechanics of patients with or without adjacent segment degeneration after spinal fusion

Study designA retrospective, single center, case-control study was performed.ObjectiveThe present study employed patient-specific biomechanical modeling to find potential biomechanical differences after spinal fusion at L4/5 in patients with and without subsequent development of adjacent segment disease (ASD).MethodsThe study population comprised patients who underwent primary spinal fusion at L4/5 and were either asymptomatic during > 4 years of follow-up (CTRL; n = 18) or underwent revision surgery for ASD at L3/4 (n = 20). Landmarks were annotated on preoperative and follow-up lateral radiographs, and specific musculoskeletal models were created using a custom-built modeling pipeline. Simulated spinal muscle activation and lumbar intervertebral shear loads in unfused segments were analyzed in upright standing and forward flexion. Differences between the pre- and postoperative conditions were computed for each patient.ResultsThe average postoperative muscle activity in the upright standing posture was 88.4% of the preoperative activity in the CTRL group (p < 0.0001), but did not significantly change from pre- to postoperatively in the ASD group (98.0%). The average shear load magnitude at the epifusional joint L3/4 during upright standing increased from pre- to postoperatively in the ASD group (+ 3.9 N, +/− 17.4 (n = 18)), but decreased in the CTRL group (− 4.6 N, +/− 23.3 (n = 20); p < 0.001).ConclusionPatient-specific biomechanical simulation revealed that spinal fusion surgery resulted in greater shear load magnitude and muscle activation and therefore greater forces at the epifusional segment in those with ASD compared with those without ASD. This is a first report of patient-specific disc load and muscle force calculation with predictive merits for ASD.

Credibility assessment of patient-specific biomechanical models to investigate proximal junctional failure in clinical cases with adult spine deformity using ASME V&V40 standard

Computational models are increasingly used to assess spine biomechanics and support surgical planning. However, varying levels of model verification and validation, along with characterization of uncertainty effects limit the level of confidence in their predictive potential. The objective was to assess the credibility of an adult spine deformity instrumentation model for proximal junction failure (PJF) analysis using the ASME V&V40:2018 framework. To assess model applicability, the surgery, erected posture, and flexion movement of actual clinical cases were simulated. The loads corresponding to PJF indicators for a group of asymptomatic patients and a group of PJF patients were compared. Model consistency was demonstrated by finding PJF indicators significantly higher for the simulated PJF vs. asymptomatic patients. A detailed sensitivity analysis and uncertainty quantification were performed to further establish the model credibility.

Virtual preoperative planning of acetabular fractures using patient-specific biomechanical simulation: A case-control study

IntroductionThe first patient-specific biomechanical model for planning the surgical reduction of acetabular fractures was developed in our institution and validated retrospectively. There are no prior studies showing its effectiveness in terms of reduction quality, operative duration and intraoperative bleeding. Therefore, we performed a case control study aiming to: 1) evaluate the effect of preoperative simulation by patient-specific biomechanical simulator on the operating time and intraoperative bleeding; 2) evaluate the effect of preoperative simulation by patient-specific biomechanical simulator on the quality of reduction. MethodAll patients operated on between January 2019 and June 2019 after planning by biomechanical simulation were included in this case-control study. Each patient included was matched to 2 controls from our database (2015–2018) according to age and fracture-type. DICOM data were extracted from the preoperative high-resolution scanners to build a three-dimensional model of the fracture by semi-automatic segmentation. A biomechanical model was built to virtually simulate the different stages of surgical reduction. Surgery was then performed according to simulation data. Surgical duration, blood loss, radiological findings and intraoperative complications were recorded, analysed and compared. ResultsThirty patients were included, 10 in the simulation group and 20 in the control group. The two groups were comparable in terms of age, time from accident to surgery, fracture-type and surgical approach. The mean operative time was significantly reduced in the simulation group: 113min±33 (60–180) versus 196min±32 (60–260) (p=0.01). Mean blood loss was significantly reduced in the simulation group: 505mL±189 (100–750) versus 745mL±130 (200–850) (p<0.01). However, no significant difference was found in the radiological results according to Matta's criteria, although an anatomical reduction was obtained for 9 patients in the simulation group (90%) versus 12 patients in the control group (60%) (p=0.26). A postoperative neurological complication was recorded in the control group (sensory deficit of the lateral cutaneous nerve of thigh). ConclusionThis study confirms the promising results of preoperative planning in acetabular trauma surgery based on patient-specific biomechanical simulation as well as its feasibility in routine clinical practice. By providing a better understanding of the fracture and its behavior, a reduction in intraoperative bleeding and in operative duration is achieved. Level of evidenceIII; case-control study.

Effect of Suboptimal Neuromuscular Control on the Risk of Massive Wear in Total Knee Replacement

The optimal neuromuscular control (muscle activation strategy that minimises the consumption of metabolic energy) during level walking is very close to that which minimises the force transmitted through the joints of the lower limbs. Thus, any suboptimal control involves an overloading of the joints. Some total knee replacement patients adopt suboptimal control strategies during level walking; this is particularly true for patients with co-morbidities that cause neuromotor control degeneration, such as Parkinson’s Disease (PD). The increase of joint loading increases the risk of implant failure, as reported in one study in PD patients (5.44% of failures at 9 years follow-up). One failure mode that is directly affected by joint loading is massive wear of the prosthetic articular surface. In this study we used a validated patient-specific biomechanical model to estimate how a severely suboptimal control could increase the wear rate of total knee replacements. Whereas autopsy-retrieved implants from non-PD patients typically show average polyethylene wear of 17 mm3 per year, our simulations suggested that a severely suboptimal control could cause a wear rate as high as of 69 mm3 per year. Assuming the risk of implant failure due to massive wear increase linearly with the wear rate, a severely suboptimal control could increase the risk associated to that failure mode from 0.1% to 0.5%. Based on these results, such increase would not be not sufficient to justify alone the higher incidence rate of revision in patients affected by Parkinson’s Disease, suggesting that other failure modes may be involved.