Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: an ex-vivo feasibility study

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.