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- Laura Stone McGuire, Angelica Fuentes, and Ali Alaraj.
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois, USA. Electronic address: lmcguir1@uic.edu.
- World Neurosurg. 2021 Oct 1; 154: 53-63.
BackgroundThe expanding use of three-dimensional (3D) printing in open vascular and endovascular neurosurgery presents a promising new tool in resident learning as well as operative planning. Recent studies have investigated the accuracy, efficacy, and practicality of 3D-printed models of patient-specific disease.ObjectiveTo review the literature exploring 3D modeling in neurovascular and endovascular surgery for training, simulation, and surgical preparation.MethodsA systematic search of the PubMed database was conducted using keywords relating to 3D printing and neurovascular or endovascular surgery. Articles were manually screened to include those that focused on resident training, surgical simulation, or preoperative planning. Information on fabrication method, materials, cost, and validation measures was collected.ResultsA total of 27 articles were identified that met inclusion criteria. Twenty-one studies used 3D printing to produce aneurysm models, 5 produced arteriovenous malformation models, and 1 produced aneurysm and arteriovenous malformation models. Stereolithography was the most common fabrication method used, with acrylonitrile butadiene styrene and VeroClearTangoPlus (Stratasys) being the most frequently used materials. The mean manufacturing cost per model was U.S. $624.83. Outcomes included model measurement accuracy, concordance of intraoperative devices with those selected preoperatively, and qualitative feedback.ConclusionsModels generated by 3D printing are anatomically accurate and aid in resident learning as well as operative planning in open vascular and endovascular neurosurgery. As advancements in printing methods are made and manufacturing costs decrease, this tool may supplement training on a wider scale in a field in which direct exposure to cases is limited.Copyright © 2021 Elsevier Inc. All rights reserved.
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