Quality Assurance in Medical 3D-Printing

Kanters, Djim; Vries, Anke de; Boon, H; Urbach, Joost; Becht, Arjen; Kooistra, Homme-Auke

doi:10.1007/978-981-10-9035-6_125

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…Such metrics are suitable only if the scaffolds are homogeneous; however, scaffold designs are becoming increasingly complex and heterogeneous [ 1 ], and thus require more robust 3D QA. General 3D printing workflows typically rely on precisely designed phantoms to gauge print quality at a certain point in time [ 7 ]. However, they are incapable of providing QA for any patient-specific scaffold.…”

Section: Discussionmentioning

confidence: 99%

“…CT scanning has been noted to be the best non-destructive characterization method based on its ability to capture large 3D volumetric structures at relatively high resolution, but there is a need for better algorithms to process the data and compare 3D-print outcomes to the theoretical design [ 6 ]. Beyond tissue engineering applications, QA assessments for 3D printing typically involve fabricating phantoms with precise and regular geometries, which can be easily validated by measuring with calipers [ 7 ]. The premise of this approach is that the deviation measured in the phantom is representative of that occurring in all subsequent prints.…”

Section: Introductionmentioning

confidence: 99%

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A robust, autonomous, volumetric quality assurance method for 3D printed porous scaffolds

et al. 2022

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Bone tissue engineering strategies aimed at treating critical-sized craniofacial defects often utilize novel biomaterials and scaffolding. Rapid manufacturing of defect-matching geometries using 3D-printing strategies is a promising strategy to treat craniofacial bone loss to improve aesthetic and regenerative outcomes. To validate manufacturing quality, a robust, three-dimensional quality assurance pipeline is needed to provide an objective, quantitative metric of print quality if porous scaffolds are to be translated from laboratory to clinical settings. Previously published methods of assessing scaffold print quality utilized one- and two-dimensional measurements (e.g., strut widths, pore widths, and pore area) or, in some cases, the print quality of a single phantom is assumed to be representative of the quality of all subsequent prints. More robust volume correlation between anatomic shapes has been accomplished; however, it requires manual user correction in challenging cases such as porous objects like bone scaffolds. Here, we designed porous, anatomically-shaped scaffolds with homogenous or heterogenous porous structures. We 3D-printed the designs with acrylonitrile butadiene styrene (ABS) and used cone-beam computed tomography (CBCT) to obtain 3D image reconstructions. We applied the iterative closest point algorithm to superimpose the computational scaffold designs with the CBCT images to obtain a 3D volumetric overlap. In order to avoid false convergences while using an autonomous workflow for volumetric correlation, we developed an independent iterative closest point (I-ICP10) algorithm using MATLAB®, which applied ten initial conditions for the spatial orientation of the CBCT images relative to the original design. Following successful correlation, scaffold quality can be quantified and visualized on a sub-voxel scale for any part of the volume.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A robust, autonomous, volumetric quality assurance method for 3D printed porous scaffolds

et al. 2022

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“…A dataset with a slice thickness of 0.4 mm and kVp of 120 can result in a model with an average deviation of 0.4 mm compared with the source structure; however, the quality drops significantly if a larger slide thickness is used. The datasets used in this study were with a slice thickness of 1 mm and kVp of 120, resulting in a model with an average deviation of ± 1.65 mm [46]. The accuracy of the segmentation can be improved via resampling by decreasing the voxel size, which improves the dimensional accuracy down to ± 0.95 mm.…”

Section: Discussionmentioning

confidence: 99%

Management of Complex Acetabular Fractures by Using 3D Printed Models

et al. 2022

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Background and Objectives: Using 3D printed models in orthopaedics and traumatology contributes to a better understanding of injury patterns regarding surgical approaches, reduction techniques, and fracture fixation methods. The aim of this study is to evaluate the effectiveness of a novel technique implementing 3D printed models to facilitate the optimal preoperative planning of the surgical treatment of complex acetabular fractures. Materials and Methods: Patients with complex acetabular fractures were assigned to two groups: (1) conventional group (n = 12) and (2) 3D printed group (n = 10). Both groups included participants with either a posterior column plus posterior wall fracture, a transverse plus posterior wall fracture, or a both-column acetabular fracture. Datasets from CT scanning were segmented and converted to STL format, with separated bones and fragments for 3D printing in different colors. Comparison between the two groups was performed in terms of quality of fracture reduction (good: equal to, or less than 2 mm displacement, and fair: larger than 2 mm displacement), functional assessment, operative time, blood loss, and number of intraoperative x-rays. Results: A significant decrease in operative time, blood loss, and number of intraoperative x-rays was registered in the 3D printed group versus the conventional one (p < 0.01), with 80% of the patients in the former having good fracture reduction and 20% having fair reduction. In contrast, 50% of the patients in the conventional group had good reduction and 50% had fair reduction. The functional score at 18-month follow-up was better for patients in the 3D printed group. Conclusions: The 3D printing technique can be considered a highly efficient and patient-specific approach for management of complex acetabular fractures, helping to restore patient′s individual anatomy after surgery.

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“…Many of the potential technical risks in the tool creation process may also affect medical 3D printing generally (for example, those related to medical imaging and 3D printing). These errors are qualitatively induced if they are due to human error, or quantitatively induced if they are the result of technical failures or limitations [55,56]. Errors by surgeons or radiologists in selecting the appropriate patient image are qualitatively induced errors, while imaging artefacts in patient scans are quantitatively induced errors.…”

Section: Ethical Risks In the Tool Creation Processmentioning

confidence: 99%

Ethical risks of AI-designed products: bespoke surgical tools as a case study

2022

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An emerging use of machine learning (ML) is creating products optimised using computational design for individual users and produced using 3D printing. One potential application is bespoke surgical tools optimised for specific patients. While optimised tool designs benefit patients and surgeons, there is the risk that computational design may also create unexpected designs that are unsuitable for use with potentially harmful consequences. We interviewed potential stakeholders to identify both established and unique technical risks associated with the use of computational design for surgical tool design and applied ethical risk analysis (eRA) to identify how stakeholders might be exposed to ethical risk within this process. The main findings of this research are twofold. First, distinguishing between unique and established risks for new medical technologies helps identify where existing methods of risk mitigation may be applicable to a surgical innovation, and where new means of mitigating risks may be needed. Second, the value of distinguishing between technical and ethical risks in such a system is that it identifies the key responsibilities for managing these risks and allows for any potential interdependencies between stakeholders in managing these risks to be made explicit. The approach demonstrated in this paper may be applied to understanding the implications of new AI and ML applications in healthcare and other high consequence domains.

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Quality Assurance in Medical 3D-Printing

Cited by 4 publications

References 14 publications

A robust, autonomous, volumetric quality assurance method for 3D printed porous scaffolds

A robust, autonomous, volumetric quality assurance method for 3D printed porous scaffolds

Management of Complex Acetabular Fractures by Using 3D Printed Models

Ethical risks of AI-designed products: bespoke surgical tools as a case study

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