Screening for adolescent idiopathic scoliosis: an information statement by the scoliosis research society international task force

Background: Routine screening of scoliosis is a controversial subject and screening efforts vary greatly around the world. METHODS: Consensus was sought among an international group of experts (seven spine surgeons and one clinical epidemiologist) using a modified Delphi approach. The consensus achieved was based on careful analysis of a recent critical review of the literature on scoliosis screening, performed using a conceptual framework of analysis focusing on five main dimensions: technical, clinical, program, cost and treatment effectiveness. FINDINGS: A consensus was obtained in all five dimensions of analysis, resulting in 10 statements and recommendations. In summary, there is scientific evidence to support the value of scoliosis screening with respect to technical efficacy, clinical, program and treatment effectiveness, but there insufficient evidence to make a statement with respect to cost effectiveness. Scoliosis screening should be aimed at identifying suspected cases of scoliosis that will be referred for diagnostic evaluation and confirmed, or ruled out, with a clinically significant scoliosis. The scoliometer is currently the best tool available for scoliosis screening and there is moderate evidence to recommend referral with values between 5 degrees and 7 degrees. There is moderate evidence that scoliosis screening allows for detection and referral of patients at an earlier stage of the clinical course, and there is low evidence suggesting that scoliosis patients detected by screening are less likely to need surgery than those who did not have screening. There is strong evidence to support treatment by bracing. INTERPRETATION: This information statement by an expert panel supports scoliosis screening in 4 of the 5 domains studied, using a framework of analysis which includes all of the World Health Organisation criteria for a valid screening procedure.

The effectiveness of scoliosis screening programs: methods for systematic review and expert panel recommendations formulation

Background: Literature on scoliosis screening is vast, however because of the observational nature of available data and methodological flaws, data interpretation is often complex, leading to incomplete and sometimes, somewhat misleading conclusions. The need to propose a set of methods for critical appraisal of the literature about scoliosis screening, a comprehensive summary and rating of the available evidence appeared essential. METHODS: To address these gaps, the study aims were: i) To propose a framework for the assessment of published studies on scoliosis screening effectiveness; ii) To suggest specific questions to be answered on screening effectiveness instead of trying to reach a global position for or against the programs; iii) To contextualize the knowledge through expert panel consultation and meaningful recommendations. The general methodological approach proceeds through the following steps: Elaboration of the conceptual framework; Formulation of the review questions; Identification of the criteria for the review; Selection of the studies; Critical assessment of the studies; Results synthesis; Formulation and grading of recommendations in response to the questions. This plan follows at best GRADE Group (Grades of Recommendation, Assessment, Development and Evaluation) requirements for systematic reviews, assessing quality of evidence and grading the strength of recommendations. CONCLUSIONS: In this article, the methods developed in support of this work are presented since they may be of some interest for similar reviews in scoliosis and orthopaedic fields.

Scoliosis curve type classification from 3D trunk image

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.

Statistical model based 3D shape prediction of postoperative trunks for non-invasive scoliosis surgery planning

One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.

A physically based trunk soft tissue modeling for scoliosis surgery planning systems

One of the major concerns of scoliotic patients undergoing spinal correction surgery is the trunk's external appearance after the surgery. This paper presents a novel incremental approach for simulating postoperative trunk shape in scoliosis surgery. Preoperative and postoperative trunk shapes data were obtained using three-dimensional medical imaging techniques for seven patients with adolescent idiopathic scoliosis. Results of qualitative and quantitative evaluations, based on the comparison of the simulated and actual postoperative trunk surfaces, showed an adequate accuracy of the method. Our approach provides a candidate simulation tool to be used in a clinical environment for the surgery planning process.

Simulation of the postoperative trunk appearance in scoliosis surgery

Persistence of external trunk asymmetry after scoliosis surgical treatment is frequent and difficult to predict by clinicians. This is a significant problem considering that correction of the apparent deformity is a major factor of satisfaction for the patients. A simulation of the correction on the external appearance would allow the clinician to illustrate to the patient the potential result of the surgery and would help in deciding on a surgical strategy that could most improve his/her appearance. We describe a method to predict the scoliotic trunk shape after a spine surgical intervention. The capability of our method was evaluated using real data of scoliotic patients. Results of the qualitative evaluation were very promising and a quantitative evaluation based on the comparison of the simulated and the actual postoperative trunk surface showed an adequate accuracy for clinical assessment. The required short simulation time also makes our approach an eligible candidate for a clinical environment demanding interactive simulations.

Reliability of a quantitative clinical posture assessment tool among persons with idiopathic scoliosis

Objective To determine overall, test–retest and inter-rater reliability of posture indices among persons with idiopathic scoliosis. Design A reliability study using two raters and two test sessions. Setting Tertiary care paediatric centre. Participants Seventy participants aged between 10 and 20 years with different types of idiopathic scoliosis (Cobb angle 15 to 60°) were recruited from the scoliosis clinic. Main outcome measures Based on the XY co-ordinates of natural reference points (e.g. eyes) as well as markers placed on several anatomical landmarks, 32 angular and linear posture indices taken from digital photographs in the standing position were calculated from a specially developed software program. Generalisability theory served to estimate the reliability and standard error of measurement (SEM) for the overall, test–retest and inter-rater designs. Bland and Altman's method was also used to document agreement between sessions and raters. Results In the random design, dependability coefficients demonstrated a moderate level of reliability for six posture indices (ϕ = 0.51 to 0.72) and a good level of reliability for 26 posture indices out of 32 (ϕ ≥ 0.79). Error attributable to marker placement was negligible for most indices. Limits of agreement and SEM values were larger for shoulder protraction, trunk list, Q angle, cervical lordosis and scoliosis angles. The most reproducible indices were waist angles and knee valgus and varus. Conclusions Posture can be assessed in a global fashion from photographs in persons with idiopathic scoliosis. Despite the good reliability of marker placement, other studies are needed to minimise measurement errors in order to provide a suitable tool for monitoring change in posture over time.

Differences in Standing and Sitting Postures of Youth with Idiopathic Scoliosis from Quantitative Analysis of Digital Photographs

The objective of this study was to explore whether differences in standing and sitting postures of youth with idiopathic scoliosis could be detected from quantitative analysis of digital photographs. Standing and sitting postures of 50 participants aged 10–20-years-old with idiopathic scoliosis (Cobb angle: 15° to 60°) were assessed from digital photographs using a posture evaluation software program. Based on the XY coordinates of markers, 13 angular and linear posture indices were calculated in both positions. Paired t-tests were used to compare values of standing and sitting posture indices. Significant differences between standing and sitting positions (p < 0.05) were found for head protraction, shoulder elevation, scapula asymmetry, trunk list, scoliosis angle, waist angles, and frontal and sagittal plane pelvic tilt. Quantitative analysis of digital photographs is a clinically feasible method to measure standing and sitting postures among youth with scoliosis and to assist in decisions on therapeutic interventions.

Is Breast Asymmetry Present in Girls with Adolescent Idiopathic Scoliosis?

Study Design Cross-sectional descriptive study. Objectives To characterize breast asymmetry (BA), as defined by breast volume difference, in girls with significant adolescent idiopathic scoliosis (AIS), using magnetic resonance imaging (MRI). Summary and Background BA is a frequent concern among girls with AIS. It is commonly believed that this results from chest wall deformity. Although many women exhibit physiological BA, the prevalence is not known in adolescents and it remains unclear if it is more frequent in AIS. Breasts vary in shape and size and many ways of measuring them have been explored. MRI shows the highest precision at defining breast tissue. Methods Thirty patients were enrolled on the basis of their thoracic curvature, skeletal and breast maturity, without regard to their perception on their BA. MRI acquisitions were performed in prone with a 1.5-Tesla system using a 16-channel breast coil. Segmentation was achieved using the ITK-SNAP 2.4.0 software and subsequently manually refined. Results The mean left breast volume (528.32 ± 205.96 cc) was greater compared with the mean right breast volume (495.18 ± 170.16 cc) with a significant difference between them. The mean BA was found to be 8.32% ± 6.43% (p < .0001). A weak positive correlation was observed between BA and thoracic Cobb angle (0.177, p = .349) as well as thoracic gibbosity angle (0.289, p = .122). The left breast was consistently larger in 65.5% of the patients. Twenty patients (66.7%) displayed BA ≥5%. Conclusions We have described BA in patients with significant AIS using MRI. This method is feasible, objective, and very precise. The majority of patients had a larger left breast, which could compound the apparent BA secondary to trunk rotation. In many cases, BA is present independently of thoracic deformity. This knowledge will assist in counseling AIS patients in regards to their concerns with BA.

Towards Non Invasive Diagnosis of Scoliosis Using Semi-supervised Learning Approach

In this paper, a new methodology for the prediction of scoliosis curve types from non invasive acquisitions of the back surface of the trunk is proposed. One hundred and fifty-nine scoliosis patients had their back surface acquired in 3D using an optical digitizer. Each surface is then characterized by 45 local measurements of the back surface rotation. Using a semi-supervised algorithm, the classifier is trained with only 32 labeled and 58 unlabeled data. Tested on 69 new samples, the classifier succeeded in classifying correctly 87.0% of the data. After reducing the number of labeled training samples to 12, the behavior of the resulting classifier tends to be similar to the reference case where the classifier is trained only with the maximum number of available labeled data. Moreover, the addition of unlabeled data guided the classifier towards more generalizable boundaries between the classes. Those results provide a proof of feasibility for using a semi-supervised learning algorithm to train a classifier for the prediction of a scoliosis curve type, when only a few training data are labeled. This constitutes a promising clinical finding since it will allow the diagnosis and the follow-up of scoliotic deformities without exposing the patient to X-ray radiations.

Noninvasive Clinical Assessment of Trunk Deformities Associated With Scoliosis

Besides the spinal deformity, scoliosis modifies notably the general appearance of the trunk resulting in trunk rotation, imbalance, and asymmetries that constitutes patients' major concern. Existing classifications of scoliosis, based on the type of spinal curve as depicted on radiographs, are currently used to guide treatment strategies. Unfortunately, even though a perfect correction of the spinal curve is achieved, some trunk deformities remain, making patients dissatisfied with the treatment received. The purpose of this study is to identify possible shape patterns of trunk surface deformity associated with scoliosis. First, trunk surface is represented by a multivariate functional trunk shape descriptor based on 3-D clinical measurements computed on cross sections of the trunk. Then, the classical formulation of hierarchical clustering is adapted to the case of multivariate functional data and applied to a set of 236 trunk surface 3-D reconstructions. The highest internal validity is obtained when considering 11 clusters that explain up to 65% of the variance in our dataset. Our clustering result shows a concordance with the radiographic classification of spinal curves in 68% of the cases. As opposed to radiographic evaluation, the trunk descriptor is 3-D and its functional nature offers a compact and elegant description of not only the type, but also the severity and extent of the trunk surface deformity along the trunk length. In future work, new management strategies based on the resulting trunk shape patterns could be thought of in order to improve the esthetic outcome after treatment, and thus patients satisfaction.

Changes in Trunk Appearance After Scoliosis Spinal Surgery and Their Relation to Changes in Spinal Measurements

Study Design Retrospective study of surgical outcome. Objectives To evaluate quantitatively the changes in trunk surface deformities after scoliosis spinal surgery in Lenke 1A adolescent idiopathic scoliosis (AIS) patients and to compare it with changes in spinal measurements. Summary of Background Data Most studies documenting scoliosis surgical outcome used either radiographs to evaluate changes in the spinal curve or questionnaires to assess patients health-related quality of life. Because improving trunk appearance is a major reason for patients and their parents to seek treatment, this study focuses on postoperative changes in trunk surface deformities. Recently, a novel approach to quantify trunk deformities in a reliable, automatic, and noninvasive way has been proposed. Methods Forty-nine adolescents with Lenke 1A idiopathic scoliosis treated surgically were included. The back surface rotation and trunk lateral shift were computed on trunk surface acquisitions before and at least 6 months after surgery. We analyzed the effect of age, height, weight, curve severity, and flexibility before surgery, length of follow-up, and the surgical technique. For 25 patients with available three-dimensional (3D) spinal reconstructions, we compared changes in trunk deformities with changes in two-dimensional (2D) and 3D spinal measurements. Results The mean correction rates for the back surface rotation and the trunk lateral shift are 18% and 50%, respectively. Only the surgical technique had a significant effect on the correction rate of the back surface rotation. Direct vertebral derotation and reduction by spine translation provide a better correction of the rib hump (22% and 31% respectively) than the classic rod rotation technique (8%). The reductions of the lumbar Cobb angle and the apical vertebrae transverse rotation explain, respectively, up to 17% and 16% the reduction of the back surface rotation. Conclusions Current surgical techniques perform well in realigning the trunk; however, the correction of the deformity in the transverse plane proves to be more challenging. More analysis on the positive effect of vertebral derotation on the rib hump correction is needed. Level of evidence III.

Prediction of scoliosis curve type based on the analysis of trunk surface topography

Scoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.