Rapid Surgeon-Led 3D Printing of Artificial Intelligence-Assisted Cylindrical Templates for Visceral Vessel Fenestration in Physician-Modified Endografts

外科医生主导的人工智能辅助3D打印技术在医生改良型血管内支架中用于内脏血管开窗的圆柱形模板

阅读:1

Abstract

Endovascular aortic aneurysm repair (EVAR) of complex abdominal aortic aneurysms (AAAs) requires precise preoperative planning to ensure proper alignment of visceral fenestrations. However, commercial planning platforms remain costly and often inaccessible for many vascular centers. Low-cost, surgeon-driven digital workflows utilizing open-source software and desktop three-dimensional (3D) printing have become practical alternatives, allowing patient-specific planning without dependence on proprietary systems. In this report, we describe a reproducible digital workflow that combines open-source imaging software for centerline extraction, anatomical segmentation, and stereolithography (STL) model creation, along with artificial intelligence (AI)-assisted scripting to generate cylindrical fenestrated templates. The anatomical and cylindrical models were processed and printed on an affordable desktop fused-deposition modeling (FDM) printer using polylactic acid (PLA) filament. The anatomical reconstruction provided spatial orientation, while the cylindrical template served as an accurate guide for physician-modified endografts (PMEGs). A 78-year-old man with an infrarenal AAA and a prior failed EVAR underwent successful reintervention using this workflow. Fenestrations for the celiac trunk, superior mesenteric artery, and renal arteries were planned along the centerline, validated against the anatomical model, and accurately transferred to the endograft using the cylindrical stencil. Final angiography confirmed aneurysm exclusion and visceral branch patency. The cylindrical template took less than two hours to print and was sterilized with low-temperature plasma before intraoperative use. This case demonstrates that a fully open-source digital workflow, integrating centerline analysis, anatomical segmentation, STL generation, and AI-assisted modeling, can produce precise and reproducible intraoperative guides for PMEG planning. The process is quick, low-cost, and scalable, providing a viable alternative for centers lacking access to commercial planning software or custom-made devices.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。