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Customized scaffolds for large bone defects using 3D-printed modular blocks from 2D-medical images

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Abstract

Additive manufacturing (AM) has revolutionized the design and manufacturing of patient-specific, three-dimensional (3D), complex porous structures known as scaffolds for tissue engineering applications. The use of advanced image acquisition techniques, image processing, and computer-aided design methods has enabled the precise design and additive manufacturing of anatomically correct and patient-specific implants and scaffolds. However, these sophisticated techniques can be time-consuming, labor-intensive, and expensive. Moreover, the necessary imaging and manufacturing equipment may not be readily available when urgent treatment is needed for trauma patients. In this study, a novel design and AM methods are proposed for the development of modular and customizable scaffold blocks that can be adapted to fit the bone defect area of a patient. These modular scaffold blocks can be combined to quickly form any patient-specific scaffold directly from two-dimensional (2D) medical images when the surgeon lacks access to a 3D printer or cannot wait for lengthy 3D imaging, modeling, and 3D printing during surgery. The proposed method begins with developing a bone surface-modeling algorithm that reconstructs a model of the patient’s bone from 2D medical image measurements without the need for expensive 3D medical imaging or segmentation. This algorithm can generate both patient-specific and average bone models. Additionally, a biomimetic continuous path planning method is developed for the additive manufacturing of scaffolds, allowing porous scaffold blocks with the desired biomechanical properties to be manufactured directly from 2D data or images. The algorithms are implemented, and the designed scaffold blocks are 3D printed using an extrusion-based AM process. Guidelines and instructions are also provided to assist surgeons in assembling scaffold blocks for the self-repair of patient-specific large bone defects.

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Acknowledgements

This research is supported by the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Global Challenges Research Fund (GCRF), Grant Number EP/R015139/1. This paper presents original research work that was conducted as part of a master's thesis at Sabanci University [20]. The authors would like to thank Sabanci University and Nanotechnology Research and Application Center.

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, 34956, Türkiye

    Anil A. Acar & Bahattin Koc

  2. 3D Bioprinting Laboratory, Sabanci University Nanotechnology Research and Application Center, Istanbul, 34956, Türkiye

    Anil A. Acar & Bahattin Koc

  3. School of Mechanical, Aerospace and Civil Engineering, Manchester Institute of Biotechnology, University of Manchester, Manchester, M13 9PL, UK

    Evangelos Daskalakis, Paulo Bartolo, Andrew Weightman & Glen Cooper

  4. Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Paulo Bartolo

  5. School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, PO1 2UP, UK

    Gordon Blunn

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  1. Anil A. Acar
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  2. Evangelos Daskalakis
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  3. Paulo Bartolo
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  4. Andrew Weightman
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  5. Glen Cooper
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  7. Bahattin Koc
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Contributions

AAA: methodology, software, investigation, writing—original draft, and visualization; BK: supervision, writing—review and editing, conceptualization, and funding acquisition; ED: conceptualization; AW, GC, GB, and PB: conceptualization and funding acquisition.

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Correspondence to Bahattin Koc.

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PB is an editorial board member for Bio-Design and Manufacturing and was not involved in the editorial review or the decision to publish this article. The authors declare that they have no conflict of interest.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Acar, A.A., Daskalakis, E., Bartolo, P. et al. Customized scaffolds for large bone defects using 3D-printed modular blocks from 2D-medical images. Bio-des. Manuf. 7, 74–87 (2024). https://doi.org/10.1007/s42242-023-00259-x

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  • DOI: https://doi.org/10.1007/s42242-023-00259-x

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