Two-Dimensional Footprint-Based Estimation of Arch Height of Chinese Children with Flatfeet
Source abstract
Abstract
Early diagnosis of pediatric flatfoot is critical to provide effective and timely orthotic intervention to prevent long-term complications. Current methods like manual measurements or three-dimensional (3D) scanning are often not ideal for rapid screening. This study addresses this gap by developing a regression model to predict foot arch height (AH) among Chinese primary schoolchildren by using simple two-dimensional (2D) footprint parameters, thus enabling orthosis prescription in an accessible and timely manner. A correlational study is conducted with 57 Hong Kong children who are 8–12 years old with flatfeet. Anthropometric footprint measurements, including four footprint indexes, Clarke’s Angle (CA), Chippaux–Smirak Index (CSI), Staheli Index (SI), and Sztriter–Godunov index (KY) are obtained from 2D footprints, whereas the arch height (AH) is measured by a caliper. One-way ANOVA is used to identify the measurements associated with flatfoot severity. A stepwise regression analysis is also used to determine the key footprint predictors of AH. The results show that the footprint indexes, heel width, ball width, medial ball length, arch depth and arch breadth significantly differentiated flatfoot severity. The stepwise regression model explains 44% of the variance by identifying the CA, heel width (HW) and arch depth (AD) as the strongest predictors of AH. The CA, HW and AD measured from the 2D footprints can serve as practical predictors of AH in Chinese flatfooted children. This study offers a transformative, accessible tool for pediatric flatfoot screening. Unlike resource-intensive 3D scanning or clinical assessments, our approach uses simple 2D footprints to estimate AH. This enables rapid, preliminary screening in diverse settings, empowering individuals to identify potential issues and seek timely professional evaluation. By democratizing access to early detection, it streamlines referrals, reduces unnecessary complex assessments, and facilitates earlier, personalized orthotic intervention for improved long-term foot health.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lower Extremity Biomechanics and Pathologies · Foot and Ankle Surgery · Hip disorders and treatments
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Fashion and Textiles, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
Laboratory for Artificial Intelligence in Design, Hong Kong Science Park, New Territories, Hong Kong, China
Funding
The Hong Kong Polytechnic University and the Laboratory for Artificial Intelligence in Design
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