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Abstract/Summary
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Introduction!#!In orthopedic surgery, 3D printing is a technology with promising medical applications. Publications show promising results in acetabular fracture surgery over the last years using 3D printing. However, only little information about the workflow and circumstances of how to properly derive the 3D printed fracture model out of a CT scan is published.!##!Materials and methods!#!We conducted a retrospective analysis of patients with acetabular fractures in a level 1 trauma center. DICOM data were preoperatively used in a series of patients with acetabular fractures. The 3D mesh models were created using 3D Slicer (https://www.slicer.org) with a newly introduced surface filtering method. The models were printed using PLA material with FDM printer. After reduction in the printed model, the acetabular reconstruction plate was bent preoperatively and sterilized. A clinical follow-up after 12 months in average was conducted with the patients.!##!Results!#!In total, 12 patients included. Mean printing time was 8:40 h. The calculated mean printing time without applying the surface filter was 25:26 h. This concludes an average printing time reduction of 65%. Mean operation time was 3:16 h, and mean blood loss was 853 ml. Model creation time was about 11 min, and mean printing time of the 3D model was 8:40 h, preoperative model reduction time was 5 min on average, and preoperative bending of the plate took about 10 min. After 12 months, patients underwent a structured follow-up. Harris Hip Score was 75.7 points, the Modified Harris Hip Score 71.6 points and the Merle d'Aubigne Score 11.1 points on average.!##!Conclusions!#!We presented the first clinical practical technique to use 3D printing in acetabular fracture surgery. By introducing a new surface filtering pipeline, we reduced printing time and cost compared to the current literature and the state of the art. Low costs and easy handling of the 3D printing workflow make it usable in nearly every hospital setting for acetabular fracture surgery.
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1000
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Hinweis
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DeepGreen-ID: 052ca6294d9247b2b31455327293d99a ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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