ArticleJournal of medical imaging (Bellingham, Wash.)2023
Three-dimensional printing accuracy analysis for medical applications across a wide variety of printers.
Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of 3D printing orientation on accuracy, properties, cost, and time efficiency of additively manufactured dental models: a systematic review.BMC oral health · 2024Pooled it
- Multi-Parametric Optimization of 3D-Printed Components.Polymers · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Three-dimensional (3D) printing has had a significant impact on patient care. However, there is a lack of standardization in quality assurance (QA) to ensure printing accuracy and precision given multiple printing technologies, variability across vendors, and inter-printer reliability issues. We investigated printing accuracy on a diverse selection of 3D printers commonly used in the medical field. Approach: 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 six months by printing the QA phantom monthly on each printer. Qualitative assessment and quantitative measurements were obtained for each printed phantom. 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: Differences in 3D printing accuracy across printers were observed. Material jetting and vat photopolymerization printers were found to be the 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. Conclusions: These results show how factors such as printing technology, vendor, and printer model can impact 3D printing accuracy, which should be appropriately considered in practice to avoid potential medical or surgical errors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.