Finite element (FE) modeling of the spine has increasingly been applied in orthopedic precision-medicine approaches. Previously published FE models of the pediatric spine growth have made simplifications in geometry of anatomical structures, material properties, and representation of vertebral growth. To address those limitations, a comprehensive FE model of a pediatric (10-year-old) osteo-ligamentous thoracic and lumbar spine (T1-L5 with intervertebral discs (IVDs) and ligaments), ribcage, and pelvis with age- and level-specific ligament properties and orthotropic region-specific vertebral growth was developed and validated. Range of motion (ROM) measures, namely lateral bending, flexion-extension, and axial rotation, of the current 10 YO FE model were generally within reported ranges of scaled in vitro adult ROM data. Changes in T1-L5 spine height, as well as kyphosis (T2-T12) and lordosis (L1-L5) angles in the current FE model for two years of growth (from ages 10 to 12 years) were within ranges reported from corresponding pediatric clinical data. The use of such comprehensive pediatric FE models can provide clinically relevant insights into normative and pathological biomechanical responses of the spine, and also contribute to the development and optimization of clinical interventions for spine deformities.

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Purpose While the C-EOS system helps organize and classify Early Onset Scoliosis (EOS) pathology, it is not data-driven and does not help achieve consensus for surgical treatment. The current study aims to create an automated method to cluster EOS patients based on pre-operative clinical indices. Methods A total of 1114 EOS patients were used for the study, with the following distribution by etiology: congenital (240), idiopathic (217), neuromuscular (417), syndromic (240). Pre-operative clinical indices used for clustering were age, major curve (Cobb) angle, kyphosis, number of levels involved in a major curve (Cobb angle) and kyphosis along with deformity index (defined as the ratio of major Cobb angle and kyphosis). Fuzzy C-means clustering was performed for each etiology individually, with one-way ANOVA performed to assess statistical significance (p < 0.05). Results The automated clustering method resulted in three clusters per etiology as the optimal number based on the highest average membership values. Statistical analyses showed that the clusters were significantly different for all the clinical indices within and between etiologies. Link to the ACT-EOS web application: https://biomed.drexel.edu/labs/obl/toolkits/act-eos-application. Conclusion An automated method to cluster EOS patients based on pre-operative clinical indices was developed identifying three unique, data-driven subgroups for each C-EOS etiology category. Adoption of such an automated clustering framework can help improve the standardization of clinical decision-making for EOS.