Assessment of malalignment at early stage in adolescent idiopathic scoliosis: a longitudinal cohort study.

INTRODUCTION: Our objective was to assess abnormalities of the odontoid-hip axis (OD-HA) angle in a mild scoliotic population to determine whether screening for malalignment would help predict the distinction between progressive and stable adolescent idiopathic scoliosis (AIS) at early stage. MATERIALS AND METHODS: All patients (non-scoliotic and AIS) underwent a biplanar X-ray between 2013 and 2020. In AIS, inclusion criteria were Cobb angle between 10° and 25°; Risser sign lower than 3; age higher than 10 years; and no previous treatment. A 3D spine reconstruction was performed, and the OD-HA was computed automatically. A reference corridor for OD-HA values in non-scoliotic subjects was calculated as the range [5th-95th percentiles]. A severity index, helping to distinguish stable and progressive AIS, was calculated and weighted according to the OD-HA value. RESULTS: Eighty-three non-scoliotic and 205 AIS were included. The mean coronal and sagittal OD-HA angles in the non-scoliotic group were 0.2° and -2.5°, whereas in AIS values were 0.3° and -0.8°, respectively. For coronal and sagittal OD-HA, 27.5% and 26.8% of AIS were outside the reference corridor compared with 10.8% in non-scoliotic (OR = 3.1 and 3). Adding to the severity index a weighting factor based on coronal OD-HA, for thoracic scoliosis, improved the positive predictive value by 9% and the specificity by 13%. CONCLUSION: Analysis of OD-HA suggests that AIS patients are almost three times more likely to have malalignment compared with a non-scoliotic population. Furthermore, analysis of coronal OD-HA is promising to help the clinician distinguish between stable and progressive thoracic scoliosis.

Prediction of trunk muscle activation and spinal forces in adolescent idiopathic scoliosis during simulated trunk motion: A musculoskeletal modelling study.

Due to lack of reference validation data, the common strategy in characterizing adolescent idiopathic scoliosis (AIS) by musculoskeletal modelling approach consists in adapting structure and parameters of validated body models of adult individuals with physiological alignments. Until now, only static postures have been replicated and investigated in AIS subjects. When aiming to simulate trunk motion, two critical factors need consideration: how distributing movement along the vertebral motion levels (lumbar spine rhythm), and if neglecting or accounting for the contribution of the stiffness of the motion segments (disc stiffness). The present study investigates the effect of three different lumbar spine rhythms and absence/presence of disc stiffness on trunk muscle imbalance in the lumbar region and on intervertebral lateral shear at different levels of the thoracolumbar/lumbar scoliotic curve, during simulated trunk motions in the three anatomical planes (flexion/extension, lateral bending, and axial rotation). A spine model with articulated ribcage previously developed in AnyBody software and adapted to replicate the spinal alignment in AIS subjects is employed. An existing dataset of 100 subjects with mild and moderate scoliosis is exploited. The results pointed out the significant impact of lumbar spine rhythm configuration and disc stiffness on changes in the evaluated outputs, as well as a relationship with scoliosis severity. Unfortunately, no optimal settings can be identified due to lack of reference validation data. According to that, extreme caution is recommended when aiming to adapt models of adult individuals with physiological alignments to adolescent subjects with scoliotic deformity.

Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations.

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr). Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr's ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values. This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running.

Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.

PURPOSE: Radiation-free systems based on dorsal surface topography can potentially represent an alternative to radiographic examination for early screening of scoliosis, based on the ability of recognizing the presence of deformity or classifying its severity. This study aims to assess the effectiveness of a deep learning model based on convolutional neural networks in directly predicting the Cobb angle from rasterstereographic images of the back surface in subjects with adolescent idiopathic scoliosis. METHODS: Two datasets, comprising a total of 900 individuals, were utilized for model training (720 samples) and testing (180). Rasterstereographic scans were performed using the Formetric4D device. The true Cobb angle was obtained from radiographic examination. The best model configuration was identified by comparing different network architectures and hyperparameters through cross-validation in the training set. The performance of the developed model in predicting the Cobb angle was assessed on the test set. The accuracy in classifying scoliosis severity (non-scoliotic, mild, and moderate category) based on Cobb angle was evaluated as well. RESULTS: The mean absolute error in predicting the Cobb angle was 6.1° ± 5.0°. Moderate correlation (r = 0.68) and a root-mean-square error of 8° between the predicted and true values was reported. The overall accuracy in classifying scoliosis severity was 59%. CONCLUSION: Despite some improvement over previous approaches that relied on spine shape reconstruction, the performance of the present fully automatic application is below that of radiographic evaluation performed by human operators. The study confirms that rasterstereography cannot be considered a valid non-invasive alternative to radiographic examination for clinical purposes.

Effect of a mattress on lumbar spine alignment in supine position in healthy subjects: an MRI study.

BACKGROUND: Humans should sleep for about a third of their lifetime and the choice of the mattress is very important from a quality-of-life perspective. Therefore, the primary aim of this study was to assess the changes of lumbar angles, evaluated in a supine position using magnetic resonance imaging (MRI), on a mattress versus a rigid surface. METHODS: Twenty healthy subjects (10 females, 10 males), aged 32.3 ± 6.5 (mean ± standard deviation), with body mass index 22.4 ± 2.9, completed three evaluations: (i) spine MRI in supine position on a mattress (MAT); (ii) spine MRI in supine position on rigid surface (CON); and (iii) biplanar radiographic imaging in standing position. The following indexes were calculated for both MAT and CON: lumbar lordosis angles L1-L5, L1-S1, L5-S1, and the sacral slope (SS). Further, pelvic incidence (PI) was calculated from the biplanar radiographic images. RESULTS: Main findings were (i) L1-L5 and SS were greater in MAT than CON (L1:L5: +2.9°; SS: +2.0°); (ii) L5-S1 was lower in MAT than CON (-1.6°); (iii) L1-S1 was greater in MAT than CON only for male subjects (+2.0°); (iv) significant and positive correlations between PI and L1-L5, L1-S1 and SS were observed in both CON and MAT. CONCLUSIONS: The use of a mattress determined small but statistically significant changes in lumbar angles. RELEVANCE STATEMENT: The use of a mattress determines small but statistically significant changes in radiological angles describing the sagittal alignment of the lumbar spine when lying in the supine position. KEY POINTS: • Lordosis angle L1-L5 was greater in MAT than in CON condition (+2.9°). • Sacral slope was greater in MAT than in CON condition (+2.0°). • Lordosis angle L5-S1 was lower in MAT than in CON condition (-1.6°).

Automatic classification of the vertebral endplate lesions in magnetic resonance imaging by deep learning model.

Introduction: A novel classification scheme for endplate lesions, based on T2-weighted images from magnetic resonance imaging (MRI) scan, has been recently introduced and validated. The scheme categorizes intervertebral spaces as "normal," "wavy/irregular," "notched," and "Schmorl's node." These lesions have been associated with spinal pathologies, including disc degeneration and low back pain. The exploitation of an automatic tool for the detection of the lesions would facilitate clinical practice by reducing the workload and the diagnosis time. The present work exploits a deep learning application based on convolutional neural networks to automatically classify the type of lesion. Methods: T2-weighted MRI scans of the sagittal lumbosacral spine of consecutive patients were retrospectively collected. The middle slice of each scan was manually processed to identify the intervertebral spaces from L1L2 to L5S1, and the corresponding lesion type was labeled. A total of 1,559 gradable discs were obtained, with the following types of distribution: "normal" (567 discs), "wavy/irregular" (485), "notched" (362), and "Schmorl's node" (145). The dataset was divided randomly into a training set and a validation set while preserving the original distribution of lesion types in each set. A pretrained network for image classification was utilized, and fine-tuning was performed using the training set. The retrained net was then applied to the validation set to evaluate the overall accuracy and accuracy for each specific lesion type. Results: The overall rate of accuracy was found equal to 88%. The accuracy for the specific lesion type was found as follows: 91% (normal), 82% (wavy/irregular), 93% (notched), and 83% (Schmorl's node). Discussion: The results indicate that the deep learning approach achieved high accuracy for both overall classification and individual lesion types. In clinical applications, this implementation could be employed as part of an automatic detection tool for pathological conditions characterized by the presence of endplate lesions, such as spinal osteochondrosis.

Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing.

STUDY DESIGN: Retrospective study. OBJECTIVES: Huge amounts of images and medical reports are being generated in radiology departments. While these datasets can potentially be employed to train artificial intelligence tools to detect findings on radiological images, the unstructured nature of the reports limits the accessibility of information. In this study, we tested if natural language processing (NLP) can be useful to generate training data for deep learning models analyzing planar radiographs of the lumbar spine. METHODS: NLP classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) model able to extract structured information from radiological reports were developed and used to generate annotations for a large set of radiographic images of the lumbar spine (N = 10 287). Deep learning (ResNet-18) models aimed at detecting radiological findings directly from the images were then trained and tested on a set of 204 human-annotated images. RESULTS: The NLP models had accuracies between 0.88 and 0.98 and specificities between 0.84 and 0.99; 7 out of 12 radiological findings had sensitivity >0.90. The ResNet-18 models showed performances dependent on the specific radiological findings with sensitivities and specificities between 0.53 and 0.93. CONCLUSIONS: NLP generates valuable data to train deep learning models able to detect radiological findings in spine images. Despite the noisy nature of reports and NLP predictions, this approach effectively mitigates the difficulties associated with the manual annotation of large quantities of data and opens the way to the era of big data for artificial intelligence in musculoskeletal radiology.

Is posteromedial translation with sublaminar bands effective in correcting axial rotation in adolescent idiopathic scoliosis surgery? A 3D reconstruction study.

PURPOSE: Hybrid constructs with sublaminar bands have recently regained popularity as an alternative to all-screw construct for correction of adolescent idiopathic scoliosis (AIS). The aim of this study is to evaluate the ability of hybrid constructs with sublaminar bands to achieve a tridimensional correction of the scoliotic deformity. Our hypothesis is that hybrid construct with sublaminar bands are able to achieve a substantial derotation of the apical vertebrae, while preserving the thoracic kyphosis. METHODS: A prospective evaluation of 50 consecutive cases (41 F, 9 M, mean age 14.7 ± 2 years) of AIS correction with hybrid construct was performed. In all cases, sublaminar bands were used at the apex of the main curve on concave side. All patients underwent pre and postoperative X-rays with EOS System, with full 3D reconstruction. Spinopelvic parameters and axial rotation of the vertebrae were measured pre and postoperatively. RESULTS: 2.7 ± 0.9 mean sublaminar bands were used per patient. Mean correction of deformity was 50 ± 9.5%. on the coronal plane. The mean axial rotation of the apical vertebra went from 18° ± 11.5° preoperatively to 9.4° ± 7.2° postoperatively (p < 0.001) with a mean derotation of 47.7%. Thoracic kyphosis went from 32.1° ± 18° preoperatively to 37.3° ± 13.1° postoperatively (p < 0.05). No intraoperative complications due to sublaminar bands were recorded. CONCLUSIONS: Hybrid construct with sublaminar band have been showed to be safe and effective in deformity correction and in maintaining or restoring thoracic kyphosis. This study showed that with sublaminar bands applied at the curve apex a substantial derotation of the apical vertebrae can be achieved.

High-Demand Spinal Deformity With Multi-Rod Constructs and Porous Fusion/Fixation Implants: A Finite Element Study.

STUDY DESIGN: Basic science (finite element analysis). OBJECTIVES: Pedicle subtraction osteotomy (PSO) at L5 is an effective treatment for sagittal imbalance, especially in select cases of patients showing kyphosis with the apex at L4-L5 but has been scarcely investigated. The aim of this study was to simulate various "high-demand" instrumentation approaches, including varying numbers of rods and sacropelvic implants, for the stabilization of a PSO at L5. METHODS: A finite element model of T10-pelvis was modified to simulate posterior fixation with pedicle screws and rods from T10 to S1, alone or in combination with an L5 PSO. Five additional configurations were then created by employing rods and novel porous fusion/fixation implants across the sacroiliac joints, in varying numbers. All models were loaded using pure moments of 7.5 Nm in flexion-extension, lateral bending, and axial rotation. RESULTS: The osteotomy resulted in a general increase in motion and stresses in posterior rods and S1 pedicle screws. When the number of rods was varied, three- and four-rod configurations were effective in limiting the maximal rod stresses; values approached those of posterior fixation with no osteotomy. Maximum stresses in the accessory rods were similar to or less than those observed in the primary rods. Multiple sacropelvic implants were effective in reducing range of motion, particularly of the SIJ. CONCLUSIONS: Multi-rod constructs and sacropelvic fixation generally reduced maximal implant stresses and motion in comparison with standard posterior fixation, suggesting a reduced risk of rod breakage and increased joint stability, respectively, when a high-demand construct is utilized for the correction of sagittal imbalance.

Spine slenderness is not an early sign of progression in adolescent idiopathic scoliosis.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine. Spine slenderness, which represents its potential instability to buckling under compressive loads, was shown to be higher in AIS patients than non-scoliotic subjects, but it is not clear at what stage of the progression this difference appeared, nor if slenderness could be used as an early sign of progression. In this study, we hypothesized that slenderness could be an early sign of progression. One-hundred thirty-eight patients and 93 non-scoliotic subjects were included. They underwent standing biplanar radiography and 3D reconstruction of the spine, which allowed computing vertebra and disc slenderness ratio. Then, patients were followed until progression of the deformity or skeletal maturity (stable patients). Vertebral slenderness ratio in AIS patients varied between 2.9 [2.7; 3.0] (T9) and 3.4 [3.2; 3.6] (T1), while disc slenderness ranged from 0.6 [0.6; 0.7] at T6-T7 to 1.2 [1.1; 1.3] at L4-L5. Slenderness ratio increased with age, while disc slenderness tended to decrease with age and Cobb angle. Slenderness was similar between progressive and stable patients, and also between patients and non-scoliotic subjects. In conclusion, spinal slenderness does not appear to be an early sign of progression. Further studies should analyse the development of slenderness during growth, and how it could be affected by non-operative treatment.

A fresh look at spinal alignment and deformities: Automated analysis of a large database of 9832 biplanar radiographs.

We developed and used a deep learning tool to process biplanar radiographs of 9,832 non-surgical patients suffering from spinal deformities, with the aim of reporting the statistical distribution of radiological parameters describing the spinal shape and the correlations and interdependencies between them. An existing tool able to automatically perform a three-dimensional reconstruction of the thoracolumbar spine has been improved and used to analyze a large set of biplanar radiographs of the trunk. For all patients, the following parameters were calculated: spinopelvic parameters; lumbar lordosis; mismatch between pelvic incidence and lumbar lordosis; thoracic kyphosis; maximal coronal Cobb angle; sagittal vertical axis; T1-pelvic angle; maximal vertebral rotation in the transverse plane. The radiological parameters describing the sagittal alignment were found to be highly interrelated with each other, as well as dependent on age, while sex had relatively minor but statistically significant importance. Lumbar lordosis was associated with thoracic kyphosis, pelvic incidence and sagittal vertical axis. The pelvic incidence-lumbar lordosis mismatch was found to be dependent on the pelvic incidence and on age. Scoliosis had a distinct association with the sagittal alignment in adolescent and adult subjects. The deep learning-based tool allowed for the analysis of a large imaging database which would not be reasonably feasible if performed by human operators. The large set of results will be valuable to trigger new research questions in the field of spinal deformities, as well as to challenge the current knowledge.

The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach.

The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index-ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a "good early outcome". A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an "excellent early outcome". The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months' follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.

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.

Accounting for Biomechanical Measures from Musculoskeletal Simulation of Upright Posture Does Not Enhance the Prediction of Curve Progression in Adolescent Idiopathic Scoliosis.

A major clinical challenge in adolescent idiopathic scoliosis (AIS) is the difficulty of predicting curve progression at initial presentation. The early detection of progressive curves can offer the opportunity to better target effective non-operative treatments, reducing the need for surgery and the risks of related complications. Predictive models for the detection of scoliosis progression in subjects before growth spurt have been developed. These models accounted for geometrical parameters of the global spine and local descriptors of the scoliotic curve, but neglected contributions from biomechanical measurements such as trunk muscle activation and intervertebral loading, which could provide advantageous information. The present study exploits a musculoskeletal model of the thoracolumbar spine, developed in AnyBody software and adapted and validated for the subject-specific characterization of mild scoliosis. A dataset of 100 AIS subjects with mild scoliosis and in pre-pubertal age at first examination, and recognized as stable (60) or progressive (40) after at least 6-months follow-up period was exploited. Anthropometrical data and geometrical parameters of the spine at first examination, as well as biomechanical parameters from musculoskeletal simulation replicating relaxed upright posture were accounted for as predictors of the scoliosis progression. Predicted height and weight were used for model scaling because not available in the original dataset. Robust procedure for obtaining such parameters from radiographic images was developed by exploiting a comparable dataset with real values. Six predictive modelling approaches based on different algorithms for the binary classification of stable and progressive cases were compared. The best fitting approaches were exploited to evaluate the effect of accounting for the biomechanical parameters on the prediction of scoliosis progression. The performance of two sets of predictors was compared: accounting for anthropometrical and geometrical parameters only; considering in addition the biomechanical ones. Median accuracy of the best fitting algorithms ranged from 0.76 to 0.78. No differences were found in the classification performance by including or neglecting the biomechanical parameters. Median sensitivity was 0.75, and that of specificity ranged from 0.75 to 0.83. In conclusion, accounting for biomechanical measures did not enhance the prediction of curve progression, thus not supporting a potential clinical application at this stage.

2-step deep learning model for landmarks localization in spine radiographs.

In this work we propose to use Deep Learning to automatically calculate the coordinates of the vertebral corners in sagittal x-rays images of the thoracolumbar spine and, from those landmarks, to calculate relevant radiological parameters such as L1-L5 and L1-S1 lordosis and sacral slope. For this purpose, we used 10,193 images annotated with the landmarks coordinates as the ground truth. We realized a model that consists of 2 steps. In step 1, we trained 2 Convolutional Neural Networks to identify each vertebra in the image and calculate the landmarks coordinates respectively. In step 2, we refined the localization using cropped images of a single vertebra as input to another convolutional neural network and we used geometrical transformations to map the corners to the original image. For the localization tasks, we used a differentiable spatial to numerical transform (DSNT) as the top layer. We evaluated the model both qualitatively and quantitatively on a set of 195 test images. The median localization errors relative to the vertebrae dimensions were 1.98% and 1.68% for x and y coordinates respectively. All the predicted angles were highly correlated with the ground truth, despite non-negligible absolute median errors of 1.84°, 2.43° and 1.98° for L1-L5, L1-S1 and SS respectively. Our model is able to calculate with good accuracy the coordinates of the vertebral corners and has a large potential for improving the reliability and repeatability of measurements in clinical tasks.

Effect of curve location on the severity index for adolescent idiopathic scoliosis: a longitudinal cohort study.

OBJECTIVES: Adolescent idiopathic scoliosis (AIS) is the most common spinal disorder in children. A severity index was recently proposed to identify the stable from the progressive scoliosis at the first standardized biplanar radiographic exam. The aim of this work was to extend the validation of the severity index and to determine if curve location influences its predictive capabilities. METHODS: AIS patients with Cobb angle between 10° and 25°, Risser 0-2, and no previous treatment were included. They underwent standing biplanar radiography and 3D reconstruction of the spine and pelvis, which allowed to calculate their severity index. Patients were grouped by curve location (thoracic, thoracolumbar, lumbar). Patients were followed up until skeletal maturity (Risser ≥ 3) or brace prescription. Their outcome was compared to the prediction made by the severity index. RESULTS: In total, 205 AIS patients were included; 82% of them (155/189, 95% confidence interval [74-90%]) were correctly classified by the index, while 16 patients were unclassified. Positive predictive ratio was 78% and negative predictive ratio was 86%. Specificity (78%) was not significantly affected by curve location, while patients with thoracic and lumbar curves showed higher sensitivity (≥ 89%) than those with thoracolumbar curves (74%). CONCLUSIONS: In this multicentric cohort of 205 patients, the severity index was used to predict the risk of progression from mild to moderate scoliosis, with similar results of typical major curve types. This index represents a novel tool to aid the clinician and the patient in the modulation of the follow-up and, for progressive patients, their decision for brace treatment. KEY POINTS: • The severity index of adolescent idiopathic scoliosis has the potential to detect patients with progressive scoliosis as early as the first exam. • Out of 205 patients, 82% were correctly classified as either stable or progressive by the severity index. • The location of the main curve had small effect on the predictive capability of the index.

The importance of curve severity, type and instrumentation strategy in the surgical correction of adolescent idiopathic scoliosis: an in silico clinical trial on 64 cases.

Adolescent idiopathic scoliosis is a three-dimensional deformity of the spine which is frequently corrected with the implantation of instrumentation with generally good or excellent clinical results; mechanical post-operative complications such as implant loosening and breakage are however relatively frequent. The rate of complications is associated with a lack of consensus about the surgical decision-making process; choices about the instrumentation length, the anchoring implants and the degree of correction are indeed mostly based on personal views and previous experience of the surgeon. In this work, we performed an in silico clinical trial on a large number of subjects in order to clarify which factors have the highest importance in determining the risk of complications by quantitatively analysing the mechanical stresses and loads in the instrumentation after the correction maneuvers. The results of the simulations highlighted the fundamental role of the curve severity, also in its three-dimensional aspect, and of the instrumentation strategy, whereas the length of the fixation had a lower importance.

Assessment of trunk muscle activation and intervertebral load in adolescent idiopathic scoliosis by musculoskeletal modelling approach.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional deformity of the spine, the aetiology and pathogenesis of which are poorly understood. Unfortunately, biomechanical data describing trunk muscle activation and intervertebral load, which can contribute to understanding the pathomechanics of the AIS spine, cannot be measured in vivo due to the invasiveness of the procedures. The present study provides the biomechanical characterization of the spinal loads in scoliotic subjects by exploiting musculoskeletal modelling approach, allowing for calculating biomechanical measures in an assigned posture. A spine model with articulated ribcage previously developed in AnyBody software was applied. The predicted outcomes were evaluated in the upright posture, depending on scoliosis severity and curve type, in a population of 132 scoliotic subjects with mild, moderate, and severe scoliosis. Radiographic-based three dimensional reconstruction of vertebral orientations and scaling of body segments and trunk muscle cross-section area guaranteed geometrical subject-specificity. Validation analysis supporting the application of the model was performed. Trunk muscles were found more activated in the convex side of the scoliotic curve, in agreement with reference in vivo measurements, with progressive increase with scoliosis severity. The intervertebral lateral shear was found positively correlated with the severity of the scoliosis, demonstrating that the transferred load is not a priori orthogonal to vertebral endplate in the frontal plane, and thus questioning the assumption of the 'follower load' approach in case of experimental or computational study on the scoliotic spine. The study opens the way for the subject-specific characterization of scoliosis in assigned loading and motion conditions.

Statistics in experimental studies on the human spine: Theoretical basics and review of applications.

Proper statistical analysis is essential in the research studies. In particular, as regards the in vitro testing of the lumbar spine, the criteria for the standardization have been extensively discussed but the use of statistics has not been reviewed. Unfortunately, cadaveric testing is a very difficult and complex experimental field, in which many factors such as collection times, costs, funding, personnel and logistic issues, availability of implant components, can determinate low sample sizes, with impacts on the statistical evaluation. Moreover, as in any other field, some errors can be commonly made in the choice of the most appropriate statistical tests. The present tutorial article provides a comprehensive overview of the theoretical basics of the statistical approach, focusing on the experimental testing of the lumbar spine. Reference values about adequate sample size and statistical power were provided. The use of statistics was reviewed in a selection of 20 papers, chosen among the most cited and representative contributions for the in vitro study of the lumbar spine. Overall, only 7 in 20 papers applied fully corrected procedures. The achieved power, calculated for the medium conventional level of the effect size, ranged from 0.13 to 0.99 but overall was found rather poor, below 0.6. The study also revealed that applying incorrect statistical tests and having low sample size can impact p-value and power, respectively, with consequences on the identification of the significant outcomes. We emphasize that this review is not intended as a mere critical analysis from a statistical perspective, but as a constructive investigation aimed to support the researchers in the challenging field of cadaveric testing. It is worth noting that the provided recommendations and reference values can be also accounted for experimental scenarios testing the other spine regions, either in human or in animal models.