发表机构
Beijing Rossum Robot Technology Co., Ltd.; Beihang University; Beijing 101 High School International Department（北京罗萨姆机器人科技有限公司; 北京航空航天大学; 北京一零一中学国际部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于统计形状模型和深度学习的骨盆骨折骶髂螺钉自动规划流程，在200例临床病例中使安全裕度提升2%、规划时间减少90%以上，临床接受率达95%。
AI 中文摘要
经皮骶髂螺钉固定是治疗不稳定骨盆骨折的一种重要微创方法。由于骶髂区域解剖结构复杂且螺钉通道狭窄，螺钉置入的准确性和安全性直接影响手术效果。因此，准确可靠的术前螺钉规划对于提高手术成功率和降低术中风险至关重要。传统的术前规划通常需要外科医生通过手动测量来确定螺钉轨迹，这是一个劳动密集型过程，且依赖于主观的临床经验。为了解决这些挑战，我们提出了一种用于骨盆骨折患者术前骶髂螺钉规划的完全自动化流程。利用患者特定的三维解剖结构，该流程自动识别安全的螺钉通道并生成个性化的置入轨迹，以支持临床术前规划。我们在200例骨盆骨折临床病例上评估了所提出的流程。与传统的手动测量相比，四种螺钉类型的安全置入通道的安全裕度增加了2%，平均规划时间减少了90%以上，临床接受率达到95%。
英文摘要
Percutaneous iliosacral screw fixation is an important minimally invasive treatment for unstable pelvic fractures. Because the sacroiliac region has complex anatomy and narrow screw corridors, the accuracy and safety of screw placement directly affect surgical outcomes. Accurate and reliable preoperative screw planning is therefore essential to improve surgical success and reduce intraoperative risks. Conventional preoperative planning typically requires surgeons to determine screw trajectories through manual measurements, a labor-intensive process that depends on subjective clinical experience. To address these challenges, we propose a fully automated pipeline for preoperative iliosacral screw planning in patients with pelvic fractures. Using patient-specific three-dimensional anatomy, the pipeline automatically identifies safe screw corridors and generates individualized insertion trajectories to support clinical preoperative planning. We evaluated the proposed pipeline on 200 clinical cases of pelvic fractures. Compared with conventional manual measurements, the safety margin of the safe insertion corridors increased by 2% across the four screw types, the mean planning time decreased by more than 90%, and the clinical acceptance rate reached 95%.
