A Hybrid-Layered System for Image-Guided Navigation and Robot Assisted Spine Surgeries

TL;DR

This paper introduces a hybrid-layered system for image-guided and robot-assisted spine surgeries that enhances precision, reduces radiation exposure, and offers scalability, validated through extensive phantom and cadaver testing.

Contribution

It presents a novel hybrid-layered architecture integrating modular and unified components for affordable, scalable, and precise image-guided spine surgery systems.

Findings

Navigation accuracy of 1.02 ± 0.34 mm

84% of screw placements graded A with navigation guidance

90% of screw placements graded A with robot assistance

Abstract

In response to the growing demand for precise and affordable solutions for Image-Guided Spine Surgery (IGSS), this paper presents a comprehensive development of a Robot-Assisted and Navigation-Guided IGSS System. The endeavor involves integrating cutting-edge technologies to attain the required surgical precision and limit user radiation exposure, thereby addressing the limitations of manual surgical methods. We propose an IGSS workflow and system architecture employing a hybrid-layered approach, combining modular and integrated system architectures in distinctive layers to develop an affordable system for seamless integration, scalability, and reconfigurability. We developed and integrated the system and extensively tested it on phantoms and cadavers. The proposed system's accuracy using navigation guidance is 1.02 0.34 mm, and robot assistance is 1.11 0.49 mm on phantoms. Observing a similar performance in cadaveric validation where 84% of screw placements were grade A, 10% were grade B using navigation guidance, 90% were grade A, and 10% were grade B using robot assistance as per the Gertzbein-Robbins scale, proving its efficacy for an IGSS. The evaluated performance is adequate for an IGSS and at par with the existing systems in literature and those commercially available. The user radiation is lower than in the literature, given that the system requires only an average of 3 C-Arm images per pedicle screw placement and verification.