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arXiv AI By Yang Gao, Sutuke Yibulayimu, Yanzhen Liu, Zian Zhao, Yudi Sang

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

Read the original on arXiv AI →

The article presents a fully automated pipeline for preoperative iliosacral screw planning in pelvic fracture patients, utilizing patient-specific 3D anatomy to identify safe screw corridors and generate individualized insertion trajectories. In a study of 200 clinical cases, the automated method increased the safety margin of safe insertion corridors by 2% across four screw types, reduced mean planning time by over 90%, and achieved a 95% clinical acceptance rate compared to conventional manual measurements.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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