Evidence map›Paper›PMID 39576468›Full record

Article3D printing in medicine2024

Insights into geometric deviations of medical 3d-printing: a phantom study utilizing error propagation analysis.

Lukas Juergensen, Robert Rischen, Julian Hasselmann, Max Toennemann, Arne Pollmanns, Georg Gosheger, Martin Schulze

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in 3D printing in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lukas JuergensenDepartment of General Orthopedics and Tumor Orthopedics, University Hospital Muenster, Münster, 48149, Germany.
Robert RischenClinic for Radiology, University Hospital Muenster, Muenster, 48149, Germany.
Julian HasselmannDepartment of General Orthopedics and Tumor Orthopedics, University Hospital Muenster, Münster, 48149, Germany.
Max ToennemannDepartment of General Orthopedics and Tumor Orthopedics, University Hospital Muenster, Münster, 48149, Germany.
Arne PollmannsMaterials Engineering Laboratory, Department of Mechanical Engineering, University of Applied Sciences Muenster, Steinburg, 48565, Germany.
Georg GoshegerDepartment of General Orthopedics and Tumor Orthopedics, University Hospital Muenster, Münster, 48149, Germany.
Martin SchulzeDepartment of General Orthopedics and Tumor Orthopedics, University Hospital Muenster, Münster, 48149, Germany. martin.schulze@ukmuenster.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of 3D-printing in medicine requires a context-specific quality assurance program to ensure patient safety. The process of medical 3D-printing involves several steps, each of which might be prone to its own set of errors. The segmentation error (SegE), the digital editing error (DEE) and the printing error (PrE) are the most important partial errors. Approaches to evaluate these have not yet been implemented in a joint concept. Consequently, information on the stability of the overall process is often lacking and possible process optimizations are difficult to implement. In this study, SegE, DEE, and PrE are evaluated individually, and error propagation is used to examine the cumulative effect of the partial errors.

methodsThe partial errors were analyzed employing surface deviation analyses. The effects of slice thickness, kernel, threshold, software and printers were investigated. The total error was calculated as the sum of SegE, DEE and PrE.

resultsThe higher the threshold value was chosen, the smaller were the segmentation results. The deviation values varied more when the CT slices were thicker and when the threshold was more distant from a value of around -400 HU. Bone kernel-based segmentations were prone to artifact formation. The relative reduction in STL file size [as a proy for model complexity] was greater for higher levels of smoothing and thinner slice thickness of the DICOM datasets. The slice thickness had a minor effect on the surface deviation caused by smoothing, but it was affected by the level of smoothing. The PrE was mainly influenced by the adhesion of the printed part to the build plate. Based on the experiments, the total error was calculated for an optimal and a worst-case parameter configuration. Deviations of 0.0093 mm ± 0.2265 mm and 0.3494 mm ± 0.8001 mm were calculated for the total error.

conclusionsVarious parameters affecting geometric deviations in medical 3D-printing were analyzed. Especially, soft reconstruction kernels seem to be advantageous for segmentation. The concept of error propagation can contribute to a better understanding of the process specific errors and enable future analytical approaches to calculate the total error based on process parameters.

Indexed as

Digital editing errorEffect of kernelEffect of slice thicknessEffect of smootingFFF-3D-printingMedical 3D-printingPatient specificPoint-of-carePrinting errorQuality assuranceSegmentation errorWarping

Identifiers

PMID39576468
PMCPMC11583775

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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.