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Despite its recent introduction into clinical medical practice, three-dimensional (3D) printing is having a significant impact on patient care by generating patient-specific 3D anatomic models and surgical guides from volumetric medical imaging data sets (e.g., high-resolution computed tomography (CT), magnetic resonance imaging (MRI), etc). These models are transforming care by providing new but not limited to methods of data visualization, patient-specific prostheses and surgical guides. However, there is a lack of standardization, particularly in terms of quality assurance (QA) to ensure that the 3D printed model accurately represents the corresponding patient’s anatomy and pathology. This becomes even more problematic given multiple printing technologies, variability across vendors, and inter printer capabilities, all of which have an impact on printing accuracy and precision. In this study, we investigated printing accuracy on a diverse selection of 3D printers commonly used in the medical field. A specially designed 3D printing QA phantom was periodically printed on 16 printers used in our practice, covering five distinct printing technologies and eight different vendors. Longitudinal data were acquired over a period of six months with the QA phantom printed monthly on each printer. Qualitative assessment and quantitative measurements were obtained for each printed phantom. The accuracy and precision were assessed by comparing quantitative measurements with reference values of the phantom. Data were then compared among printer models, vendors, and printing technologies. Longitudinal trends were also analyzed. Results revealed differences in accuracy across printers. It was found that material jetting and vat photopolymerization printers were most accurate. Printers using the same 3D printing technology but from different vendors also showed differences in accuracy, most notably between vat photopolymerization printers from two different vendors. Furthermore, differences in accuracy were found between printers from the same vendor using the same printing technology, but different models/generations. These results show how factors such as printing technology, vendor, and model can impact 3D printing accuracy, which should be appropriately considered in practice to avoid potential detrimental consequences.
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