Optimizing the design and production of custom 3D-printed knee orthoses.

Abstract

BACKGROUND: Traditional methods for producing custom-made orthoses are often time-consuming, labor-intensive, and reliant on manual processes, which limit both scalability and the degree of individualization. The development of 3D scanning technologies, computer-aided design (CAD), and additive manufacturing offers a promising alternative enabling patient-specific solutions with greater precision, speed, and efficiency. This study aimed to create an algorithm for automating the design process of personalized knee orthoses based on 3D scanning and intended for 3D printing production. METHODS: A parametric modeling workflow was developed in the Rhino environment using the Grasshopper plug-in to streamline personalized knee orthoses creation. The process began with acquiring high-quality 3D scans using Structure Sensor Mark II scanner mounted on an iPad with 3DsizeMe software. The parametric algorithm was transformed into an autonomous Rhino plug-in using C# language and RhinoCommon API. As part of Post-Market Clinical Follow-up (PMCF), three participants with knee joint disorders used orthoses for one month. Assessment used a 5-point scale (1 = poor, 5 = excellent). Personalized orthoses were manufactured using powder-bed fusion technology with PA11 CF nylon powder reinforced with carbon fibers. RESULTS: Design time was reduced from approximately 8 h to 10,3 ± 1,4 min. In Grasshopper prototype phase, average design time was 26,7 ± 4,5 min. Following the implementation of the Rhino plug-in, the design time was further reduced to approximately 10 min. The tool was shown to meet user requirements and fulfill its intended purpose. All three PMCF participants rated orthoses positively, reporting high comfort, effective stabilization, increased physical activity, and overall satisfaction with functionality and appearance. Participants P1 and P2 noted a large increase in physical activity, with P1 indicating pain reduction that increased mobility. CONCLUSIONS: This study demonstrates that the combined use of Rhino and Grasshopper provides an effective platform for parametric design of personalized knee orthoses based on patient-specific 3D scans. The workflow reduced design time to approximately 10,3 ± 1,4 min, highlighting potential for routine clinical applications. This reduction is economically significant, lowering labor costs and implementation thresholds for personalized orthotic solutions in clinical practice.