Research in Orthopedic Computer Science, Balgrist University Hospital, University of Zurich, Lengghalde 5, Zurich, 8008, Switzerland.
Department of Orthopedics, Balgrist University Hospital, University of Zurich, Zurich, 8008, Switzerland.
BMC Musculoskelet Disord. 2024 Nov 19;25(1):925. doi: 10.1186/s12891-024-08052-2.
With the increasing number of surgeries utilizing spinal instrumentation, three-dimensional surgical navigation aims to improve the accuracy of implant placement. However, its widespread clinical adaption has been hindered by factors such as high radiation exposure and interference with standard surgical workflows.
X23D is a novel AI-based fluoroscopy reconstruction technique that generates a 3D anatomical model of the spine from only four fluoroscopy images. Based on this technology, we developed a prototype for the surgical navigation of pedicle screws placement of the lumbar spine, visualizing the 3D-reconstructed spine anatomy and the surgical drill position in real-time. An ex-vivo study was conducted to compare the accuracy of the X23D-based navigation approach with fluoroscopy-aided freehand instrumentation. Five board-certified surgeons placed pedicle screws on six human torsi within a realistic surgical environment. Breach rate, site and extent (Gertzbein-Robbins) were evaluated in postoperative CT scans, as well as execution time, radiation dose, and user experience. Specimens, operating side, and surgeon were randomised.
Forty-nine pedicle screws (n = 24 × 23D, n = 25 2D-fluoroscopy) were evaluated, with six breaches occurring in the control group, one of which was considered clinically significant (medial breach grade C). Five breaches with one clinically significant breach were observed in the X23D group. Breach rate, execution time for each lumbar level (X23D 167 s vs. control 156 s), radiation dose (X23D 33.26 mGy vs. control 49.5 mGy), and user experience did not reveal significant differences (p > 0.05) between the groups.
Spinal navigation using the X23D-based approach shows promise and performs well in a realistic surgical ex-vivo setting. With further refinements, its accuracy is expected to match clinical-grade navigation systems while reducing radiation dose.
随着越来越多的脊柱手术采用内固定器械，三维手术导航旨在提高植入物放置的准确性。然而，由于高辐射暴露和对标准手术流程的干扰等因素，其广泛的临床应用受到了阻碍。
X23D 是一种新型基于人工智能的透视重建技术，仅从四张透视图像即可生成脊柱的三维解剖模型。基于这项技术，我们开发了一种用于腰椎经皮螺钉放置手术导航的原型，实时显示三维重建的脊柱解剖结构和手术钻头位置。进行了一项离体研究，比较了基于 X23D 的导航方法与透视辅助徒手器械的准确性。五名认证的外科医生在现实的手术环境中在六个人体标本的腰椎上放置经皮螺钉。在术后 CT 扫描中评估了穿透率、位置和范围（Gertzbein-Robbins），以及执行时间、辐射剂量和用户体验。标本、手术侧和外科医生均为随机分组。
评估了 49 枚经皮螺钉（n=24×X23D，n=25 例透视辅助徒手），对照组中有 6 枚螺钉发生穿透，其中 1 枚为临床显著穿透（内侧 C 级穿透）。X23D 组观察到 5 枚穿透，其中 1 枚为临床显著穿透。两组间的穿透率、每个腰椎水平的执行时间（X23D 组 167s 比对照组 156s）、辐射剂量（X23D 组 33.26mGy 比对照组 49.5mGy）和用户体验均无显著差异（p>0.05）。
基于 X23D 的脊柱导航在真实的手术离体环境中表现出良好的效果和潜力。通过进一步改进，其准确性有望与临床级导航系统相匹配，同时降低辐射剂量。
