Three-Dimensional Surface Topography for Entropy-Based Comparison of Sagittal Postural Classifications in Children
Year: 2026
Type: article
Study design: Observational Study
Abstract: Assessment of sagittal spinal alignment depends on the classification system applied, as different approaches use distinct category structures and normative thresholds. This study evaluated the differentiation of sagittal postural profiles in children using classical and nine-type posture classification systems based on three-dimensional surface topography and Shannon entropy analysis. A total of 952 children aged 10-12 years (478 girls and 474 boys) underwent body posture assessment using the DIERS Formetric III 4D rasterstereographic system. Thoracic kyphosis and lumbar lordosis angles were used for posture classification according to the classical five-type model and the nine-type Wilczyński typology. Category distributions were compared using the chi-square test, Cramér’s V coefficient, and Shannon entropy analysis. The classical classification concentrated the majority of participants in two categories (round back, 53.6%; normal posture, 40.5%), whereas the nine-type typology distributed participants more evenly across posture profiles. A strong association was observed between the classification systems (Cramér’s V = 0.69; p < 0.001). Entropy was higher for the nine-type typology (H = 1.86) than for the classical classification (H = 1.17), indicating greater differentiation of sagittal posture profiles. These findings demonstrate that the choice of classification system substantially influences posture profile distribution. Shannon entropy provides an objective quantitative measure of classification differentiation and may support the evaluation of posture classification systems based on three-dimensional surface topography.
Source: Sensors
DOI: 10.3390/s26175408
Institution: Jan Kochanowski University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7204475653