Design and Physical Properties of 3-Dimensional Printed Models Used for Neurointervention: A Systematic Review of the Literature.

2020 
BACKGROUND: Three-dimensional (3D) printing has revolutionized training, education, and device testing. Understanding the design and physical properties of 3D-printed models is important. OBJECTIVE: To systematically review the design, physical properties, accuracy, and experimental outcomes of 3D-printed vascular models used in neurointervention. METHODS: We conducted a systematic review of the literature between January 1, 2000 and September 30, 2018. Public/Publisher MEDLINE (PubMed), Web of Science, Compendex, Cochrane, and Inspec databases were searched using Medical Subject Heading terms for design and physical attributes of 3D-printed models for neurointervention. Information on design and physical properties like compliance, lubricity, flow system, accuracy, and outcome measures were collected. RESULTS: A total of 23 articles were included. Nine studies described 3D-printed models for stroke intervention. Tango Plus (Stratasys) was the most common material used to develop these models. Four studies described a population-representative geometry model. All other studies reported patient-specific vascular geometry. Eight studies reported complete reconstruction of the circle of Willis, anterior, and posterior circulation. Four studies reported a model with extracranial vasculature. One prototype study reported compliance and lubricity. Reported circulation systems included manual flushing, programmable pistons, peristaltic, and pulsatile pumps. Outcomes included thrombolysis in cerebral infarction, post-thrombectomy flow restoration, surgical performance, and qualitative feedback. CONCLUSION: Variations exist in the material, design, and extent of reconstruction of vasculature of 3D-printed models. There is a need for objective characterization of 3D-printed vascular models. We propose the development of population representative 3D-printed models for skill improvement or device testing.
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