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More than meets the eye? evaluating 3D printing for progressive collapsing foot deformity classification.
Wolfram Grün1, Enrico Pozzessere2, Emily J Luo2
1Department of Orthopaedic Surgery, Duke University School of Medicine, Durham, NC, USA; Department of Orthopaedic Surgery, Østfold Hospital Trust, Grålum, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Three-dimensional (3D) printing aids in classifying progressive collapsing foot deformity (PCFD) stages and classes. While intraobserver reliability is high, interobserver agreement varies, suggesting 3D models enhance understanding but need integration with other diagnostic tools.
Area of Science:
- Orthopedics
- Medical Imaging
- Biomechanical Engineering
Background:
- Progressive collapsing foot deformity (PCFD) is a complex, multiplanar foot condition.
- The 2020 classification system categorizes PCFD into two stages and five classes.
- Diagnosing certain PCFD classes, like Class D, presents challenges.
Purpose of the Study:
- To evaluate the inter- and intraobserver reliability of PCFD classification using 3D-printed models.
- To assess the utility of 3D printing as a tool for anatomical assessment in PCFD.
Main Methods:
- Retrospective analysis of 60 patients undergoing WBCT for PCFD.
- Creation of 80% scale 3D-printed models from WBCT data.
- Independent assessment of PCFD classes A-E by five foot and ankle surgeons, with repeated assessments for intra-observer reliability.
Main Results:
- The most common PCFD class combinations were ABCD (30%) and ABC (23%).
- Intra-observer reliability was perfect for Class A (Kappa=1.00) and lowest for Class B (Kappa=0.40).
- Interobserver reliability ranged from slight for Class B (Kappa=0.10) to fair for Class D (Kappa=0.38).
Conclusions:
- 3D-printed models show moderate-to-perfect intraobserver agreement but variable interobserver agreement for PCFD classification.
- 3D printing enhances spatial understanding of PCFD but may limit diagnostic consistency if used alone.
- Further research is needed to determine the additive value of 3D printing combined with WBCT for PCFD diagnosis.
