Xiong Xu, Liu Jia-Ming, Lu William Weijia, Yang Ke-Di, Qi Huan, Liu Zhi-Li, Zhang Ning, Huang Shan-Hu
Department of Orthopedics, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University.
Jiangxi Provincial Key Laboratory of Spine and Spinal Cord Diseases, Nanchang.
Clin Spine Surg. 2025 Apr 1;38(3):154-160. doi: 10.1097/BSD.0000000000001687. Epub 2024 Sep 3.
Retrospective cohort study.
To evaluate the effectiveness of pedicle screw trajectory planning based on artificial intelligence (AI) software in patients with different levels of bone mineral density (BMD).
AI-based pedicle screw trajectory planning has potential to improve pullout force (POF) of screws. However, there is currently no literature investigating the efficacy of AI-based pedicle screw trajectory planning in patients with different levels of BMD.
The patients were divided into 5 groups (group A-E) according to their BMD. The AI software utilizes lumbar spine CT data to perform screw trajectory planning and simulate AO screw trajectories for bilateral L3-5 vertebral bodies. Both screw trajectories were subdivided into unicortical and bicortical modes. The AI software automatically calculating the POF and pullout risk of every screw trajectory. The POF and risk of screw pullout for AI-planned screw trajectories and AO standard trajectories were compared and analyzed.
Forty-three patients were included. For the screw sizes, AI-planned screws were greater in diameter and length than those of AO screws ( P <0.05). In groups B-E, the AI unicortical trajectories had a POF of over 200N higher than that of AO unicortical trajectories. POF was higher in all groups for the AI bicortical screw trajectories compared with the AO bicortical screw trajectories ( P <0.05). AI unicortical trajectories in groups B-E had a lower risk of screw pullout compared with that of AO unicortical trajectories ( P <0.05).
AI unicortical screw trajectory planning for lumbar surgery in patients with BMD of 40-120 mg/cm 3 can significantly improve screw POF and reduce the risk of screw pullout.
回顾性队列研究。
评估基于人工智能（AI）软件的椎弓根螺钉轨迹规划在不同骨密度（BMD）水平患者中的有效性。
基于AI的椎弓根螺钉轨迹规划有提高螺钉拔出力（POF）的潜力。然而，目前尚无文献研究基于AI的椎弓根螺钉轨迹规划在不同BMD水平患者中的疗效。
根据BMD将患者分为5组（A - E组）。AI软件利用腰椎CT数据进行螺钉轨迹规划，并模拟双侧L3 - 5椎体的AO螺钉轨迹。两种螺钉轨迹均细分为单皮质和双皮质模式。AI软件自动计算每个螺钉轨迹的POF和拔出风险。比较并分析AI规划的螺钉轨迹和AO标准轨迹的POF及螺钉拔出风险。
纳入43例患者。对于螺钉尺寸，AI规划的螺钉直径和长度大于AO螺钉（P <0.05）。在B - E组中，AI单皮质轨迹的POF比AO单皮质轨迹高200N以上。与AO双皮质螺钉轨迹相比，所有组中AI双皮质螺钉轨迹的POF更高（P <0.05）。与AO单皮质轨迹相比，B - E组中AI单皮质轨迹的螺钉拔出风险更低（P <0.05）。
对于骨密度为40 - 120mg/cm³的患者，腰椎手术采用AI单皮质螺钉轨迹规划可显著提高螺钉POF并降低螺钉拔出风险。
