A novel method for prediction of postoperative global sagittal alignment based on full-body musculoskeletal modeling and posture optimization

Evaluation of ground reaction forces and centers of pressure predicted by AnyBody Modeling System during load reaching/handling activities and effects of the prediction errors on model-estimated spinal loads

Full-body and lower-extremity human musculoskeletal models require feet ground reaction forces (GRFs) and centers of pressure (CoPs) as inputs to predict muscle forces and joint loads. GRFs/CoPs are traditionally measured via floor-mounted forceplates that are usually restricted to research laboratories thus limiting their applicability in real occupational and clinical setups. Alternatively, GRFs/CoPs can be estimated via inverse dynamic approaches as also implemented in the Anybody Modeling System (AnyBody Technology, Aalborg, Denmark). The accuracy of Anybody in estimating GRFs/CoPs during load-handling/reaching activities and the effect of its prediction errors on model-estimated spinal loads remain to be investigated. Twelve normal- and over-weight individuals performed total of 480 static load-handling/reaching activities while measuring (by forceplates) and predicting (by AnyBody) their GRFs/CoPs. Moreover, the effects of GRF/CoP prediction errors on the estimated spinal loads were evaluated by inputting measured or predicted GRFs/CoPs into subject-specific musculoskeletal models. Regardless of the subject groups (normal-weight or overweight) and tasks (load-reaching or load-handling), results indicated great agreements between the measured and predicted GRFs (normalized root-mean-squared error, nRMSEs < 14% and R2 > 0.90) and between their model-estimated spinal loads (nRMSEs < 14% and R2 > 0.83). These agreements were good but relatively less satisfactory for CoPs (nRMSEs < 17% and 0.57 < R2 < 0.68). The only exception, requiring a more throughout investigation, was the situation when the ground-foot contact was significantly reduced during the activity. It appears that occupational/clinical investigations performed in real workstation/clinical setups with no access to forceplates may benefit from the AnyBody GRF/CoP prediction tools for a wide range of load-reaching/handling activities.

Patient-Specific Surgical Correction of Adolescent Idiopathic Scoliosis: A Systematic Review

The restoration of sagittal alignment is fundamental to the surgical correction of adolescent idiopathic scoliosis (AIS). Despite established techniques, some patients present with inadequate postoperative thoracic kyphosis (TK), which may increase the risk of proximal junctional kyphosis (PJK) and imbalance. There is a lack of knowledge concerning the effectiveness of patient-specific rods (PSR) with measured sagittal curves in achieving a TK similar to that planned in AIS surgery, the factors influencing this congruence, and the incidence of PJK after PSR use. This is a systematic review of all types of studies reporting on the PSR surgical correction of AIS, including research articles, proceedings, and gray literature between 2013 and December 2023. From the 28,459 titles identified in the literature search, 81 were assessed for full-text reading, and 7 studies were selected. These included six cohort studies and a comparative study versus standard rods, six monocentric and one multicentric, three prospective and four retrospective studies, all with a scientific evidence level of 4 or 3. They reported a combined total of 355 AIS patients treated with PSR. The minimum follow-up was between 4 and 24 months. These studies all reported a good match between predicted and achieved TK, with the main difference ranging from 0 to 5 degrees, p > 0.05, despite the variability in surgical techniques and the rods' properties. There was no proximal junctional kyphosis, whereas the current rate from the literature is between 15 and 46% with standard rods. There are no specific complications related to PSR. The exact role of the type of implants is still unknown. The preliminary results are, therefore, encouraging and support the use of PSR in AIS surgery.

Customized design and biomechanical property analysis of 3D-printed tantalum intervertebral cages

BACKGROUND

Intervertebral cages used in clinical applications were often general products with standard specifications, which were challenging to match with the cervical vertebra and prone to cause stress shielding and subsidence.

OBJECTIVE

To design and fabricate customized tantalum (Ta) intervertebral fusion cages that meets the biomechanical requirements of the cervical segment.

METHODS

The lattice intervertebral cages were customized designed and fabricated by the selective laser melting. The joint and muscle forces of the cervical segment under different movements were analyzed using reverse dynamics method. The stress characteristics of cage, plate, screws and vertebral endplate were analyzed by finite element analysis. The fluid flow behaviors and permeability of three lattice structures were simulated by computational fluid dynamics. Compression tests were executed to investigate the biomechanical properties of the cages.

RESULTS

Compared with the solid cages, the lattice-filled structures significantly reduced the stress of cages and anterior fixation system. In comparison to the octahedroid and quaddiametral lattice-filled cages, the bitriangle lattice-filled cage had a lower stress shielding rate, higher permeability, and superior subsidence resistance ability.

CONCLUSION

The inverse dynamics simulation combined with finite element analysis is an effective method to investigate the biomechanical properties of the cervical vertebra during movements.

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads

Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To estimate muscle forces and joint loads, these models require body posture during the activity under consideration. Posture is usually measured via video-camera motion tracking approaches that are time-consuming, costly, and/or limited to laboratories. Alternatively, posture-prediction tools based on artificial intelligence can be trained using measured postures of several subjects performing many activities. We aimed to use our previous posture-prediction artificial neural network (ANN), developed based on many measured static postures, to predict posture during dynamic lifting activities. Moreover, effects of the ANN posture-prediction errors on dynamic spinal loads were investigated using subject-specific musculoskeletal models. Seven individuals each performed twenty-five lifting tasks while their full-body three-dimensional posture was measured by a 10-camera Vicon system and also predicted by the ANN as functions of the hand-load positions during the lifting activities. The measured and predicted postures (i.e., coordinates of 39 skin markers) and their model-estimated L5-S1 loads were compared. The overall root-mean-squared-error (RMSE) and normalized (by the range of measured values) RMSE (nRMSE) between the predicted and measured postures for all markers/tasks/subjects was equal to 7.4 cm and 4.1 %, respectively (R2 = 0.98 and p < 0.05). The model-estimated L5-S1 loads based on the predicted and measured postures were generally in close agreements as also confirmed by the Bland-Altman analyses; the nRMSE for all subjects/tasks was < 10 % (R2 > 0.7 and p > 0.05). In conclusion, the easy-to-use ANN can accurately predict posture in dynamic lifting activities and its predicted posture can drive musculoskeletal models.

Clinical Validation of a Novel Musculoskeletal Modeling Framework to Predict Postoperative Sagittal Alignment

STUDY DESIGN

A retrospective radiographic and biomechanical analysis of 108 thoracolumbar fusion patients from two clinical centers.

OBJECTIVE

This study aimed to determine the validity of a computational framework for predicting postoperative patient posture based on preoperative imaging and surgical data in a large clinical sample.

SUMMARY OF BACKGROUND DATA

Short-term and long-term studies on thoracolumbar fusion patients have discussed that a preoperative predictive model would benefit surgical planning and improve patient outcomes. Clinical studies have shown that postoperative alignment changes at the pelvis and intact spine levels may negatively affect postural balance and quality of life. However, it remains challenging to predict such changes preoperatively because of confounding surgical and patient factors.

MATERIALS AND METHODS

Patient-specific musculoskeletal models incorporated weight, height, body mass index, age, pathology-associated muscle strength, preoperative sagittal alignment, and surgical treatment details. The sagittal alignment parameters predicted by the simulations were compared with those observed radiographically at a minimum of three months after surgery.

RESULTS

Pearson correlation coefficients ranged from r=0.86 to 0.95, and mean errors ranged from 4.1° to 5.6°. The predictive accuracies for postoperative spinopelvic malalignment (pelvic incidence minus lumbar lordosis>10°) and sagittal imbalance parameters (TPA>14°, T9PA>7.4°, or LPA>7.2°) were between 81% and 94%. Patients treated with long fusion (greater than five segments) had relatively lower prediction errors for lumbar lordosis and spinopelvic mismatch than those in the local and short groups.

CONCLUSIONS

The overall model performance with long constructs was superior to those of the local (one to two segments) and short (three to four segments) fusion cases. The clinical framework is a promising tool in development to enhance clinical judgment and to help design treatment strategies for predictable surgical outcomes.

LEVEL OF EVIDENCE

3.

Multibody Models of the Thoracolumbar Spine: A Review on Applications, Limitations, and Challenges

Numerical models of the musculoskeletal system as investigative tools are an integral part of biomechanical and clinical research. While finite element modeling is primarily suitable for the examination of deformation states and internal stresses in flexible bodies, multibody modeling is based on the assumption of rigid bodies, that are connected via joints and flexible elements. This simplification allows the consideration of biomechanical systems from a holistic perspective and thus takes into account multiple influencing factors of mechanical loads. Being the source of major health issues worldwide, the human spine is subject to a variety of studies using these models to investigate and understand healthy and pathological biomechanics of the upper body. In this review, we summarize the current state-of-the-art literature on multibody models of the thoracolumbar spine and identify limitations and challenges related to current modeling approaches.

The Simulation of Muscles Forces Increases the Stresses in Lumbar Fixation Implants with Respect to Pure Moment Loading

Simplified loading conditions such as pure moments are frequently used to compare different instrumentation techniques to treat spine disorders. The purpose of this study was to determine if the use of realistic loading conditions such as muscle forces can alter the stresses in the implants with respect to pure moment loading. A musculoskeletal model and a finite element model sharing the same anatomy were built and validated against in vitro data, and coupled in order to drive the finite element model with muscle forces calculated by the musculoskeletal one for a prescribed motion. Intact conditions as well as a L1-L5 posterior fixation with pedicle screws and rods were simulated in flexion-extension and lateral bending. The hardware stresses calculated with the finite element model with instrumentation under simplified and realistic loading conditions were compared. The ROM under simplified loading conditions showed good agreement with in vitro data. As expected, the ROMs between the two types of loading conditions showed relatively small differences. Realistic loading conditions increased the stresses in the pedicle screws and in the posterior rods with respect to simplified loading conditions; an increase of hardware stresses up to 40 MPa in extension for the posterior rods and 57 MPa in flexion for the pedicle screws were observed with respect to simplified loading conditions. This conclusion can be critical for the literature since it means that previous models which used pure moments may have underestimated the stresses in the implants in flexion-extension and in lateral bending.

Biomechanical effects of lumbar fusion surgery on adjacent segments using musculoskeletal models of the intact, degenerated and fused spine

Adjacent segment disorders are prevalent in patients following a spinal fusion surgery. Postoperative alterations in the adjacent segment biomechanics play a role in the etiology of these conditions. While experimental approaches fail to directly quantify spinal loads, previous modeling studies have numerous shortcomings when simulating the complex structures of the spine and the pre/postoperative mechanobiology of the patient. The biomechanical effects of the L4–L5 fusion surgery on muscle forces and adjacent segment kinetics (compression, shear, and moment) were investigated using a validated musculoskeletal model. The model was driven by in vivo kinematics for both preoperative (intact or severely degenerated L4–L5) and postoperative conditions while accounting for muscle atrophies. Results indicated marked changes in the kinetics of adjacent L3–L4 and L5–S1 segments (e.g., by up to 115% and 73% in shear loads and passive moments, respectively) that depended on the preoperative L4–L5 disc condition, postoperative lumbopelvic kinematics and, to a lesser extent, postoperative changes in the L4–L5 segmental lordosis and muscle injuries. Upper adjacent segment was more affected post-fusion than the lower one. While these findings identify risk factors for adjacent segment disorders, they indicate that surgical and postoperative rehabilitation interventions should focus on the preservation/restoration of patient’s normal segmental kinematics.

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.