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Selecting Appropriate 3D Scanning Technologies for Prosthetic Socket Design and Transtibial Residual Limb Shape Characterisation




amputee, CAD/CAM, consistency, plaster casting, prosthetic socket, rectification, shape error, transtibial, volume error


Introduction: Plaster casting and manual rectification represent the benchmark prosthetic socket design method. 3D technologies have increasing potential for prosthetic limb design and fabrication, especially for enhancing access to these services in lower and middle income countries (LMICs). However, the community has a responsibility to verify the efficacy of these new digital technologies. This study’s objective was to assess the repeatability of plaster casting in vivo, specifically for clinically-relevant residuum shape and landmark capture, and to compare this with three clinically-used 3D scanners. Materials and Methods: A comparative reliability assessment of casting and 3D scanning was conducted in eleven participants with established transtibial amputation. For each participant, two positive moulds were cast by a prosthetist and digitised using a white light 3D surface scanner. Between casts, each participant’s residuum was scanned. The deviation between scan volumes, cross-sections and shapes was calculated. Results: 95% of the clinically-relevant socket shape surface area had a deviation between manual casts <2.87mm (S.D. 0.44mm). The average deviation by surface area was 0.18mm (S.D. 1.72mm). The repeatability coefficient of casting was 46.1ml (3.47%) for volume, and 9.6mm (3.53%) for perimeters. For all clinically-meaningful measures, greater reliability was observed for the Omega scanner, and worse for the Sense and iSense scanners, although it was observed that the Sense scanner performance was comparable to casting (95th percentile shape consistency). Conclusions: This study provides a platform to appraise new clinical shape capture technologies in the context of best practice in manual plaster casting, and starts the conversation of which 3D scanning devices are most appropriate for different types of clinical use. The methods and benchmark results may support prosthetists in acquiring and applying their clinical experience, as part of their continuing professional development.


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