• Amin Komeili, Lindsey Westover, Eric C Parent, Marwan El-Rich, and Samer Adeeb.
• Department of Civil and Environmental Engineering, University of Alberta, Markin/CNRL Natural Resources Engineering Facility, 9105 116th St, Edmonton, Alberta, Canada T6G 2W2. Electronic address: akomeili@ualberta.ca.
• Spine J. 2015 Apr 1; 15 (4): 743-51.

Background ContextAt 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.PurposeTo 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/SettingCross-sectional study in a specialized scoliosis clinic.Patient SampleIn 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 MeasuresPatients with ΔCobb≥5° and ΔCobb<5° were categorized into progression and nonprogression groups, respectively.MethodsThe 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.ResultsThe 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.ConclusionsUsing 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.Copyright © 2015 Elsevier Inc. All rights reserved.

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