ScolioClass: data-driven development of a new classification tool to evaluate adolescent idiopathic scoliosis optically diagnosed

Adolescent idiopathic scoliosis (AIS) is traditionally assessed and classified using radiographic methods that rely on Cobb angle measurements and qualitative curve modifiers, exposing patients to repeated radiation and offering limited sensitivity to subtle three-dimensional (3D) deformities. We developed <i>ScolioClass</i>, a non-invasive, data-driven classification tool that harnesses 3D optical surface scanning and continuous indices, capturing curvature severity, directionality, and sagittal balance, to evaluate spinal deformities in 94 patients with AIS. By comparing <i>ScolioClass</i> descriptions with the established Lenke classification, we observed a statistically significant association (<i>χ</i> ² ≈ 29.0, df = 6, <i>p</i> < 0.001) with 72.3% overall agreement. A significant association was also found between sagittal modifiers and <i>ScolioClass</i> kyphosis-lordosis categories (<i>χ</i> ² ≈ 48.4, df = 3, <i>p</i> < 0.0001) with 68.1% agreement. Notably, <i>ScolioClass</i> detected mild curves and lordotic patterns that were often overlooked by Lenke criteria. These findings demonstrate that <i>ScolioClass</i> provides radiation-free, quantitative 3D assessment of AIS with potential for automated analysis and individualized treatment planning. Further validation is warranted for clinical integration.