Ramadanov-Zabler Safe Zone for Sacroiliac Screw Placement: A CT-Based Computational Pilot Study.

Abstract

Background/Objectives: Posterior pelvic ring fractures are severe injuries requiring surgical stabilization, often through sacroiliac (SI) screw fixation. However, improper screw placement poses risks of neurovascular injury and implant failure. Defining a precise safe zone for screw placement is crucial to improving surgical accuracy and reducing complications. Methods: A computational study was conducted using a CT scan of a 75-year-old male patient to establish a safe zone for SI screw placement. Manual segmentation and 3D modeling techniques were used to analyze bone density distribution. A 2D lateral projection of the sacrum was generated to identify high-density regions optimal for screw placement. While the general principle of targeting areas of higher bone density for screw insertion is well established, this study introduces a novel computational method to define and visualize such a safe zone. The resulting region, termed the Ramadanov-Zabler Safe Zone, was delineated based on this analysis to ensure maximal intraosseous fixation with minimal risk of cortical breaches. Results: A high-resolution 3D model of the sacral region was successfully generated. Standard thresholding methods for segmentation proved ineffective due to low bone density, necessitating a freehand approach. The derived 2D projection revealed regions of higher bone density, which were defined as the Ramadanov-Zabler Safe Zone for screw insertion. This zone correlates with areas providing the best structural integrity, thereby reducing risks associated with screw misplacement. Additionally, intraoperative and postoperative imaging from a representative case is included to illustrate the translational feasibility of the proposed technique. Conclusions: The Ramadanov-Zabler Safe Zone offers a reproducible, CT-based computational approach to guide for SI screw placement, enhancing surgical precision and patient safety. This CT-based computational approach provides a standardized reference for preoperative planning, minimizing neurovascular complications and improving surgical outcomes. This pilot technique is supported by preliminary clinical imaging that demonstrates feasibility for intraoperative application. Further validation across diverse patient populations is recommended to confirm its clinical applicability.