Modular design workflow for 3D printable bioresorbable patient-specific bone scaffolds: extended features and clinical validation

Front Bioeng Biotechnol. 2024 Nov 19:12:1404481. doi: 10.3389/fbioe.2024.1404481. eCollection 2024.

Abstract

A previously in-house developed patient-specific scaffold design workflow was extended with new features to overcome several limitations and to broaden its adaptability to diverse bone defects, thereby enhancing its fit for routine clinical use. It was applied to three clinical cases for further validation. A virtual surgical resection tool was developed to remove regions of the bone defect models. The minor cavity fill module enabled the generation of scaffold designs with smooth external surfaces and the segmental defect fill module allowed a versatile method to fill a segmental defect cavity. The boundary representation method based surgical approach module in the original workflow was redeveloped to use functional representation, eliminating previously seen resolution dependant artefacts. Lastly, a method to overlay the scaffold designs on computed tomography images of the defect for design verification by the surgeon was introduced. The extended workflow was applied to two ongoing clinical case studies of a complex bilateral femoral defect and a humerus defect, and also to a case of a large volume craniomaxillofacial defect. It was able to successfully generate scaffolds without any obstructions to their surgical insertion which was verified by digital examination as well as using physical 3D printed models. All produced surface meshes were free from 3D printing mesh errors. The scaffolds designed for the ongoing cases were 3D printed and successfully surgically implanted, providing confidence in the extended modular workflow's ability to be applied to a broad range of diverse clinical cases.

Keywords: additive manufacturing; generative design; parametric design; scaffold design workflow; scaffold-guided bone regeneration.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Australian Research Council (ARC) Industrial Transformation Training Centre for Multiscale 3D Imaging, Modelling, and Manufacturing [IC 180100008], and the Jamieson Trauma Institute (PhD scholarship for Buddhi Herath), a collaboration of Metro North Hospital and Health Service and the Motor Accident Insurance Commission. Lastly, the authors gratefully acknowledge the support of the Alexander von Humboldt Foundation and the Queensland University of Technology, jointly funding a Feodor Lynen Research Fellowship of the Alexander von Humboldt Foundation awarded to Markus Laubach.