Development and Evaluation of an Automated Computational Approach for the Precise Placement of Pedicle Screws in Spinal Surgery Leveraging Three-Dimensional Point Cloud Registration Methods

This study focuses on enhancing the precision and efficacy of pedicle screw placement in spinal surgeries, particularly for patients with osteoporosis. It emphasizes the importance of accurate screw positioning to maximize pullout strength and biomechanical efficacy. The research highlights the relationship between CT values, Bone Mineral Density (BMD), and the Young’s modulus of bone tissue, suggesting that higher CT values, indicative of denser bone, lead to stronger mechanical properties. This understanding is crucial for assessing bone health, especially in osteoporosis and fracture risk analysis. The study introduces an innovative automated planning method for pedicle screw insertion in spinal vertebrae, beneficial for osteoporotic patients. This method uses PointNet++ combined with a Siamese network for semi-supervised segmentation of vertebrae in CT images, converting them into point clouds for individualized planning. The approach aims to improve accuracy and efficiency in pedicle screw placement, especially in complex or osteoporotic vertebrae. The method was validated using osteoporotic in vitro models, demonstrating its potential effectiveness for surgeons facing challenges in pedicle screw placement in osteoporotic patients. The complete workflow begins with semi-supervised vertebra segmentation using a Siamese PointNet++ architecture. The extracted vertebra point cloud is then aligned with a predefined pedicle screw model via an adaptively tuned Super4PCS registration algorithm to generate patient-specific trajectories. The paper also reviews traditional surgical path planning, which relied on surgeons’ experience and intuition, and the shift towards Computer-Aided Design (CAD) and Virtual Reality (VR) technologies for pre-operative planning. Despite these advancements, challenges remain in accurately simulating tissue properties and managing physiological variations. The study proposes a bifurcated approach to automated trajectory planning: segmenting individual vertebrae and planning pedicle screw implantation for each segmented vertebra. The method involves converting CT scans into 3D point clouds, using PointNet++ and the Siamese method for vertebra segmentation, and registering the pedicle screw point cloud with the vertebra model for precise surgical planning. The study demonstrates the method’s feasibility through clinical data from Shengjing Hospital, showing high applicability and consistency in surgical path planning, particularly in the lumbar and lower thoracic regions. The planning results were consistent with surgeons’ experience, indicating the algorithm’s adaptability and stability. Note to Practitioners—Pedicle screw placement is a critical procedure in spinal surgery, especially for osteoporotic patients where achieving strong fixation is essential. Traditional methods depend heavily on the surgeon’s expertise and the use of intraoperative fluoroscopy, which can be both time-consuming and susceptible to human error. Recent advancements propose an automated computational approach that combines deep learning techniques, such as PointNet++ and Siamese networks, for semi-supervised vertebral segmentation. This method also employs an enhanced Super4PCS algorithm for registration-based trajectory planning, aiming to provide a more accurate, consistent, and efficient strategy for pedicle screw placement. This innovative approach offers several key benefits: Automated Vertebral Segmentation and Trajectory Planning: By reducing the need for manual intervention, this method delivers reliable and reproducible paths for pedicle screw insertion based on CT imaging. Enhanced Precision and Safety: By identifying optimal screw trajectories in high-density bone regions through CT value analysis, the approach maximizes pullout strength and minimizes the risk of vertebral perforation, which is particularly beneficial for osteoporotic patients. Clinical Validation and Feasibility: Validated using clinical data from Shengjing Hospital, the study demonstrated alignment with expert surgical decisions. The deviation from planned trajectories in actual surgeries was less than 1 degree, confirming high accuracy. Integrating this method into surgical navigation systems can assist orthopedic surgeons in preoperative planning and provide real-time intraoperative guidance. This integration can lead to minimized surgical risks and complications, reduced radiation exposure by decreasing fluoroscopy usage, and improved surgical workflow efficiency, making spinal surgeries faster and more predictable.