Three-Dimensional Surface Topography for Entropy-Based Comparison of Sagittal Postural Classifications in Children

Sensor-based three-dimensional surface topography provides non-invasive quantitative measurements of sagittal spinal alignment, but categorical posture profiles also depend on the thresholds and category structure used to transform continuous measurements. This cross-sectional methodological study compared an operationalised five-category classification with the nine-type Wilczyński typology using the same sensor-derived thoracic kyphosis and lumbar lordosis measurements obtained with the DIERS Formetric III 4D rasterstereographic system. A total of 952 children aged 10-12 years (478 girls, 474 boys) were assessed. Category distributions were summarised descriptively, cross-classified in a 9 × 5 contingency table, and evaluated using Cramér's V and Shannon entropy. The five-category classification concentrated 94.1% of participants in two categories, whereas the nine-type typology produced a more dispersed and even distribution. Normalised Shannon entropy was higher for the nine-type typology (0.85 vs. 0.57), and Cramér's V was 0.55. Because both systems were deterministic transformations of the same sensor-derived measurements, these statistics were interpreted descriptively. The findings show that classification thresholds and category architecture substantially influence categorical representations derived from identical sensor-based measurements. Greater granularity and higher entropy indicate distributional differentiation, not superior clinical validity; independent external validation is required to establish clinical relevance.