Automated Screw Planning for Reduced Pelvic Fractures Based on Statistical Shape Models and Deep Learning

2026-09-29

AI Summary

This study addresses the limitations of preoperative planning for percutaneous screw fixation of pelvic fractures, which traditionally relies on subjective experience, is time-consuming, and offers constrained safety margins. To overcome these challenges, this work proposes a fully automated, patient-specific planning framework that integrates statistical shape models with deep learning techniques. By leveraging patient-specific three-dimensional anatomical imaging, the method automatically identifies safe osseous corridors and generates optimal screw insertion trajectories, thereby achieving a paradigm shift from manual measurement to intelligent planning. Clinical validation on 200 cases demonstrates that the proposed system reduces planning time by over 90%, improves the safety margin by 2%, and achieves a clinical acceptance rate of 95%, significantly enhancing both the efficiency and safety of surgical planning.

Abstract

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%.

Problem

• Pelvic fracture
• Iliosacral screw planning
• Preoperative planning
• Screw trajectory
• Minimally invasive surgery

Innovation

• Automated screw planning
• Statistical shape models
• Deep learning
• Pelvic fractures
• Percutaneous iliosacral screw fixation