Bone density optimized pedicle screw insertion.

Background: Spinal fusion is the most common surgical treatment for the management of degenerative spinal disease. However, complications such as screw loosening lead to painful pseudoarthrosis, and are a common reason for revision. Optimization of screw trajectories to increase implant resistance to mechanical loading is essential. A recent optimization method has shown potential for determining optimal screw position and size based on areas of high bone elastic modulus (E-modulus). Aim: The aim of this biomechanical study was to verify the optimization algorithm for pedicle screw placement in a cadaveric study and to quantify the effect of optimization. The pull-out strength of pedicle screws with an optimized trajectory was compared to that of a traditional trajectory. Methods: Twenty-five lumbar vertebrae were instrumented with pedicle screws (on one side, the pedicle screws were inserted in the traditional way, on the other side, the screws were inserted using an optimized trajectory). Results: An improvement in pull-out strength and pull-out strain energy of the optimized screw trajectory compared to the traditional screw trajectory was only observed for E-modulus values greater than 3500 MPa cm3. For values of 3500 MPa cm3 or less, optimization showed no clear benefit. The median screw length of the optimized pedicle screws was significantly smaller than the median screw length of the traditionally inserted pedicle screws, p < 0.001. Discussion: Optimization of the pedicle screw trajectory is feasible, but seems to apply only to vertebrae with very high E-modulus values. This is likely because screw trajectory optimization resulted in a reduction in screw length and therefore a reduction in the implant-bone interface. Future efforts to predict the optimal pedicle screw trajectory should include screw length as a critical component of potential stability.

Bone density optimized pedicle screw instrumentation improves screw pull-out force in lumbar vertebrae.

Pedicle screw instrumentation is performed in the surgical treatment of a wide variety of spinal pathologies. A common postoperative complication associated with this procedure is screw loosening. It has been shown that patient-specific screw fixation can be automated to match standard clinical practice and that failure can be estimated preoperatively using computed tomography images. Hence, we set out to optimize three-dimensional preoperative planning to achieve more mechanically robust screw purchase allowing deviation from intuitive, standard screw parameters. Toward this purpose, we employed a genetic algorithm optimization to find optimal screw sizes and trajectories by maximizing the CT derived bone mechanical properties. The method was tested on cadaveric lumbar vertebrae (L1 to L5) of four human spines (2 female/2 male; age range 60-78 years). The main boundary conditions were the predefined, level-dependent areas of possible screw entry points, as well as the automatically located pedicle structures. Finite element analysis was used to compare the genetic algorithm output to standard clinical planning of screw positioning in terms of the simulated pull-out strength. The genetic algorithm optimization successfully found screw sizes and trajectories that maximize the sum of the Young's modulus within the screw's volume for all 40 pedicle screws included in this study. Overall, there was a 26% increase in simulated pull-out strength for optimized compared to traditional screw trajectories and sizes. Our results indicate that optimizing pedicle screw instrumentation in lumbar vertebrae based on bone quality measures improves screw purchase as compared to traditional instrumentation.

Shear-stress sensing by PIEZO1 regulates tendon stiffness in rodents and influences jumping performance in humans.

Athletic performance relies on tendons, which enable movement by transferring forces from muscles to the skeleton. Yet, how load-bearing structures in tendons sense and adapt to physical demands is not understood. Here, by performing calcium (Ca2+) imaging in mechanically loaded tendon explants from rats and in primary tendon cells from rats and humans, we show that tenocytes detect mechanical forces through the mechanosensitive ion channel PIEZO1, which senses shear stresses induced by collagen-fibre sliding. Through tenocyte-targeted loss-of-function and gain-of-function experiments in rodents, we show that reduced PIEZO1 activity decreased tendon stiffness and that elevated PIEZO1 mechanosignalling increased tendon stiffness and strength, seemingly through upregulated collagen cross-linking. We also show that humans carrying the PIEZO1 E756del gain-of-function mutation display a 13.2% average increase in normalized jumping height, presumably due to a higher rate of force generation or to the release of a larger amount of stored elastic energy. Further understanding of the PIEZO1-mediated mechanoregulation of tendon stiffness should aid research on musculoskeletal medicine and on sports performance.

Patient-specific statistical shape modeling for optimal spinal sagittal alignment in lumbar spinal fusion.

PURPOSE: The present study compared patients developing ASD after L4/5 spinal fusion with a control group using a patient-specific statistical shape model (SSM) to find alignment-differences between the groups. METHODS: This study included patients who had undergone spinal fusion at L4/5 and either remained asymptomatic (control group; n = 25, follow-up of > 4 years) or required revision surgery for epifusional ASD (n = 22). Landmarks on preoperative and postoperative lateral radiographs were annotated, and the optimal spinal sagittal alignment was calculated for each patient. The two-dimensional distance from the SSM-calculated optimum to the actual positions before and after fusion surgery was compared. RESULTS: Postoperatively, the additive mean distance from the SSM-calculated optimum was 86.8 mm in the ASD group and 67.7 mm in the control group (p = 0.119). Greater differences were observed between the groups with a larger distance to the ideal in patients with ASD at more cranial levels. Significant difference between the groups was seen postoperatively in the vertical distance of the operated segment L4. The patients with ASD (5.69 ± 3.0 mm) had a significant greater distance from the SSM as the control group (3.58 ± 3.5 mm, p = 0.034). CONCLUSION: Patients with ASD requiring revision after lumbar spinal fusion have greater differences from the optimal spinal sagittal alignment as an asymptomatic control group calculated by patient-specific statistical shape modeling. Further research might help to understand the value of SSM, in conjunction with already established indexes, for preoperative planning with the aim of reducing the risk of ASD. LEVEL OF EVIDENCE I: Diagnostic: individual cross-sectional studies with consistently applied reference standard and blinding.

Automated Pipeline to Generate Anatomically Accurate Patient-Specific Biomechanical Models of Healthy and Pathological FSUs.

State-of-the-art preoperative biomechanical analysis for the planning of spinal surgery not only requires the generation of three-dimensional patient-specific models but also the accurate biomechanical representation of vertebral joints. The benefits offered by computational models suitable for such purposes are still outweighed by the time and effort required for their generation, thus compromising their applicability in a clinical environment. In this work, we aim to ease the integration of computerized methods into patient-specific planning of spinal surgery. We present the first pipeline combining deep learning and finite element methods that allows a completely automated model generation of functional spine units (FSUs) of the lumbar spine for patient-specific FE simulations (FEBio). The pipeline consists of three steps: (a) multiclass segmentation of cropped 3D CT images containing lumbar vertebrae using the DenseVNet network, (b) automatic landmark-based mesh fitting of statistical shape models onto 3D semantic segmented meshes of the vertebral models, and (c) automatic generation of patient-specific FE models of lumbar segments for the simulation of flexion-extension, lateral bending, and axial rotation movements. The automatic segmentation of FSUs was evaluated against the gold standard (manual segmentation) using 10-fold cross-validation. The obtained Dice coefficient was 93.7% on average, with a mean surface distance of 0.88 mm and a mean Hausdorff distance of 11.16 mm (N = 150). Automatic generation of finite element models to simulate the range of motion (ROM) was successfully performed for five healthy and five pathological FSUs. The results of the simulations were evaluated against the literature and showed comparable ROMs in both healthy and pathological cases, including the alteration of ROM typically observed in severely degenerated FSUs. The major intent of this work is to automate the creation of anatomically accurate patient-specific models by a single pipeline allowing functional modeling of spinal motion in healthy and pathological FSUs. Our approach reduces manual efforts to a minimum and the execution of the entire pipeline including simulations takes approximately 2 h. The automation, time-efficiency and robustness level of the pipeline represents a first step toward its clinical integration.

Spinal sagittal alignment goals based on statistical modelling and musculoskeletal simulations.

The definition of target alignment for spinal fusion surgery follows anatomical criteria and strongly relies on surgical experience. However, the optimal patient-specific alignment often remains unknown. Statistical models could provide information about physiological alignments, and musculoskeletal models are powerful tools to investigate biomechanics. We aimed to statistically predict alignments and hypothesized they would be biomechanically favorable. A statistical model was trained with 60 annotated radiographs to predict physiological sagittal alignment based on position of femoral heads and sacrum. Predicted alignments for 11 back pain patients were clinically evaluated in terms of balance and compared to Original alignments. The normative ranges for spinal balance parameters were obtained from Surgimap™. Musculoskeletal loads were furthermore simulated in upright standing and 30° forward flexion, using alignment-specific musculoskeletal models. For the majority of Predicted alignments (n = 9) at least two of three investigated balance parameters were within the normative range, as opposed to the minority of the Original alignments (n = 4). Predicted alignments resulted in significantly lowered overall muscle activity and compressive loads (all levels, both postures). Shear force magnitudes in upright standing decreased significantly at levels L1L2 (-68 N) and L2L3 (-69 N) and clearly yet not significantly at L3L4 (-39 N) and L4L5 (-152 N). Shear loads at level L5S1 remained the same. In flexed postures identical trends were observed. The statistical model was able to predict spinal alignments that led to both improved balance and reduced musculoskeletal loads. Further studies are needed to investigate clinical validity of such models.