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Adolescent Idiopathic Scoliosis (AIS) is a 3D spinal deformity characterized by curvature and rotation of the spine. Markerless surface topography (ST) analysis has been proposed for diagnosing and monitoring AIS to reduce the X-ray radiation exposure to patients. This method captures scans of the cosmetic deformity of the torso using visible, radiation-free light. The asymmetry analysis of the torso, represented as a deviation contour map with deviation patches outlining the areas of cosmetic asymmetries, has previously been shown to predict the severity and progression of the condition in comparison with radiographs, by using classification trees. While the classification results were promising, it was reported that some mild curves were erroneously diagnosed. Furthermore, this approach is highly sensitive to threshold values selected in the decision trees. Therefore, this study aims to define a custom Neighbourhood Classifier algorithm for AIS classification to improve the accuracy, sensitivity, and specificity of predicting curve severity and curve progression in AIS. Curve severity was predicted with 80% accuracy (sensitivity = 81%; specificity = 79%) for thoracic-thoracolumbar curves and 72% (sensitivity = 93%; specificity = 53%) for lumbar curves. This represents an improvement over the previous method with curve severity accuracies of 77% and 63% for thoracic-thoracolumbar and lumbar curves, respectively. Additionally, curve progression was predicted with 93% accuracy (sensitivity = 83%; specificity = 95%) representing a substantial improvement over the previous method with an accuracy of 59%. The current method has shown the potential to further reduce radiation exposure for AIS patients by avoiding X-rays for mild and non-progressive curves identified using ST analysis. [ABSTRACT FROM AUTHOR]

The ability to assess geometric asymmetry in the torsos of individuals is important for detecting Adolescent Idiopathic Scoliosis (AIS). A markerless technique using Surface Topography (ST) has been introduced as a non-invasive alternative to standard diagnostic radiographs. The technique has been used to identify asymmetry patterns associated with AIS. However, the presence and nature of asymmetries in the healthy population has not been properly studied. The purpose of this study is therefore to identify asymmetries and potential relationships to development factors such as age, gender, hand dominance and unilateral physical activity in healthy adolescents. Full torso scans of 83 participants were analyzed. Using Geomagic, deviation contour maps (DCMs) were created by reflecting the torso along the best plane of sagittal symmetry with each spectrum normalized. Two classes of asymmetry were observed: twist and thickness each with subgroupings. Averaged interobserver and intraobserver Kappas for twist subgroupings were 0.84 and 0.84, respectively, and for thickness subgroupings were 0.53 and 0.63 respectively. Further significant relationships were observed between specific types of asymmetry and gender such as females displaying predominately twist asymmetry, and males with thickness asymmetry. However, no relationships were found between type of asymmetry and age, hand dominance or unilateral physical activity. Understanding asymmetries in healthy subjects will continue to enhance assessment ability of the markerless ST technique. [ABSTRACT FROM AUTHOR]

Study Design: A validation study.Objective: The aim of this study was to independently validate the diagnostic accuracy of surface topography (ST) classification trees to identify curve severity and progression using a new sample of data in participants with adolescent idiopathic scoliosis (AIS).Summary Of Background Data: Radiographs for diagnosing and monitoring AIS involve harmful radiation exposure repeated at successive clinical visits. Classification trees using a novel ST technique have been proposed to determine curve severity and progression noninvasively that could be used to monitor scoliosis.Methods: Forty-five adolescents with AIS treated nonoperatively, with ST scans and radiographs at baseline and follow-up (1 year later), were recruited from a scoliosis clinic. The Cobb angle (CA) from radiographs determined curve severity as mild (10° < CA < 25°) or moderate/severe (CA ≥ 25°) and progression as an increase >5°.ST scans were analyzed to calculate the best plane of symmetry and associated deviation color map. Root mean squares and maximum deviation were calculated for each area of asymmetry. ST measurements were analyzed using two published decision trees developed to maximize sensitivity and negative predictive value. Curves were classified as mild or moderate/severe and curve progression was predicted. Accuracy statistics were calculated to evaluate performance.Results: For curve severity, sensitivity and specificity were 95% and 35%, respectively. Negative and positive predictive values were 90% and 53%, respectively, with an accuracy of 61%. For curve progression, sensitivity and specificity were 73% and 44%, respectively. Negative and positive predictive values were 83% and 30%, respectively, with an accuracy of 51%. Assuming that mild and nonprogressive curves would not require an x-ray, the use of ST decision trees could eliminate 31% of x-rays.Conclusion: Decision trees showed strong negative predictive values and sensitivity suggesting it may be possible to safely use ST asymmetry analysis with validated decision trees to reduce x-rays in patients with mild and nonprogressive curves.Level Of Evidence: 2. [ABSTRACT FROM AUTHOR]

Background context At first visit and each clinical follow-up session, patients with adolescent idiopathic scoliosis (AIS) undergo radiographic examination, from which the Cobb angle is measured. The cumulative exposure to X-ray radiation justifies efforts in developing noninvasive methods for scoliosis monitoring. Purpose To determine the capability of the three-dimensional markerless surface topography (ST) asymmetry analysis to detect ≥5° progression in the spinal curvature in patients with AIS over 1-year follow-up interval. Study design/setting Cross-sectional study in a specialized scoliosis clinic. Patient sample In this study, baseline and 1-year follow-up full torso ST scans of 100 patients with AIS were analyzed using three-dimensional markerless asymmetry analysis. Outcome measures Patients with ΔCobb≥5° and ΔCobb<5° were categorized into progression and nonprogression groups, respectively. Methods The ST scan of each full torso was analyzed to calculate the best plane of symmetry by minimizing the distances between the torso and its reflection about the plane of symmetry. Distance between the torso and its reflection was measured and displayed as deviation color maps. The difference of ST measurements between two successive acquisitions was used to determine if the scoliosis has progressed at least 5° or not. The classification tree technique was implemented using the local deformity of the torso in the thoracic–thoracolumbar (T–TL) and lumbar (L) regions to categorize curves into progression and nonprogression groups. The change in maximum deviation and root mean square of the deviations in the torso were the parameters effective in capturing the curve progression. Funding for this research is provided by the Scoliosis Research Society, and Women and Children's Health Research Institute. Results The classification model detected 85.7% of the progression and 71.6% of the nonprogression cases. The resulting false-negative rate of 4% for T–TL curves, representing the proportion of undetected progressions, confirmed that the technique shows promise to monitor the progression of T–TL scoliosis curves. Although 100% L curves with progression were detected using the deviation color maps of the torsos, because of the small number of analyzed L curves, further research is needed before the efficiency of the method in capturing the L curves with progression is confirmed. Conclusions Using the developed classification tree for the patients analyzed in this study, 43% of nonprogression cases between two visits would not have to undergo an X-ray examination. [ABSTRACT FROM AUTHOR]

Abstract: Visible surface asymmetries such as uneven shoulders, waist and hips, shoulder height differences and a shoulder blade prominence are often the most troublesome features associated with adolescent scoliosis. Treatment considerations are influenced by the severity and changes over time of these asymmetries. Outcomes are judged on how well the asymmetries are improved towards a normal trunk shape. In this paper, a deformable self organizing feature map (SOFM) is used as a geometric surface reconstruction tool to model the torso surface of subjects with and without scoliosis. The proposed parameterization technique provides a means of quantifying the surface asymmetries and assessing the changes due to either natural history or the effects of treatment. For evaluation 10 control subjects without scoliosis and 10 adolescents with scoliosis were scanned and their torsos were reconstructed. This preliminary study demonstrates that in around 5min a torso scan with 60,000 data points can be transformed into a 2562 nodes mesh using SOFM. The accuracy of the final mesh is around 1.40mm on average. The high accuracy and speed of this technique, makes it well suitable to be used in a clinical setting to assess surface features of subjects with scoliosis. [Copyright &y& Elsevier]

Abstract: Background context: Adolescent idiopathic scoliosis (AIS) affects 2% to 4% of the population and predominantly affects female individuals. The scoliosis researchers and clinical communities use the “Cobb angle” obtained from anterior-posterior radiographs as the standard assessment tool for scoliosis. However, excessive radiation exposure over consecutive visits during the growing years increases the risk of cancer in young patients with AIS. Surface topography (ST) is a noninvasive method that is being investigated as an alternative tool for scoliosis assessment. The necessity of applying markers by skilled operators, which is time consuming and a potential area for errors, is one of the main limitations of these methods. Purpose: This study introduces a three-dimensional markerless analysis technique for assessing torso asymmetry in AIS and a system for classifying patients based on this technique. The intra/interobserver and test-retest reliability and validity of the classification system was assessed. Study design: A novel three-dimensional analysis technique of ST data of patients with scoliosis and its clinical applications. Methods: Full-torso ST scans of 46 patients with AIS (Cobb angle: 34±15°, curve types: Lenke 1, 3, and 5) and five healthy subjects were used for analysis. The best plane of symmetry, dividing the torso into left and right, was calculated for each scan. The deviation between the original torso and its reflection with respect to the best plane of symmetry was illustrated using deviation contour maps. The subjects were visually classified into three main groups and six subgroups based on the number and location of the asymmetry contours. A second baseline scan and a 1-year follow-up scan were analyzed for 15 subjects and reliability of the method was assessed using kappa coefficients. Funding for this research is provided by the Scoliosis Research Society, Women and Children's Health Research Institute, and the Natural Sciences and Engineering Research Council of Canada. Results: The intraobserver reliability of the group classification demonstrated excellent agreement with mean kappa coefficient of 0.85. The multiobserver kappa value of 0.62 was attained in the interobserver reliability test conducted among four observers classifying 46 subjects in three groups. The test-retest reliability of the method was assessed. Mean kappa values of 0.99 and 0.83 were achieved for group (three groups) and subgroup (six subgroups) classifications, respectively. The classification system showed good reliability when five observers classified the first baseline and the 1-year follow-up scans. Conclusions: A novel method to examine torso asymmetry in patients with AIS is presented, using noninvasive ST scans and a visually intuitive asymmetry map. Distinct patterns of asymmetry were identified allowing patients to be classified into three groups, with six subgroups based on their asymmetry map with very good to excellent reliability. The presented technique shows promise to provide a noninvasive tool for assessment and monitoring of AIS. [Copyright &y& Elsevier]