Patient-specific musculoskeletal modeling to enhance preoperative planning for pelvic fracture reduction

摘要

Pelvic fracture surgery is a highly complex and skill-dependent procedure due to adjacent neurovascular structures. These challenges often result in prolonged operative time, elevated complication rates, and repeated reduction attempts. To support surgeons in overcoming these difficulties, our study introduces a rapid modeling approach that enables precise preoperative planning and quantitative evaluation of reduction strategies. We developed an automated patient-specific modeling method that integrates Statistical Shape Models with the personalized modeling modules of the OpenSim software’s Application Programming Interface for generating personalized musculoskeletal models. Using this approach, we rapidly reconstructed reduction models for 10 patients (age range: 49-72) with pelvic fractures and validated the results against clinical reduction force data. Here we show that the SMAG framework generates patient-specific models 78% faster than manual methods while reducing reconstruction errors to below 7.7%. Validation against clinical data demonstrates force prediction with an average error of 13.8%, within clinically acceptable limits (<10 N vs. typical 15–25 N traction force). Optimal reduction paths identified reduced peak forces from 555.4 N to 97.4 N. Our method transforms pelvic fracture management by translating surgical expertise into quantifiable, data-rich biomechanical parameters. This data-driven framework not only enables optimized surgical planning for both robotic and manual procedures but also provides a high-resolution quantitative basis for AI-enhanced decision support. By shifting the field from a subjective art to an objective science, our approach standardizes surgical outcomes and paves the way for intelligent systems that deliver more precise and higher-quality surgical planning. Surgery to repair a broken pelvis is very difficult and risky because the bones are near important nerves and blood vessels. To help surgeons plan these operations more safely, we created a computer tool that can quickly build a custom model of a patient’s unique injury. Our tool uses software to analyze medical scans and create a digital map of the bones. We tested it on data from ten patients and found that it is much faster and more accurate than previous methods. Most importantly, it can simulate different ways to perform the surgery and show surgeons which method requires the least force, making the procedure much safer. This research is a step towards making complex surgery more predictable and less dependent on a surgeon’s guesswork. In the future, such tools could help surgeons consistently choose the best plan for each patient, leading to better outcomes and faster recovery. Liu et al. develop an automated method (SMAG) to rapidly generate patient-specific musculoskeletal models for pelvic fracture surgery. Modeling efficiency is improved by 78% and reduces errors to below 7.7%, enabling precise biomechanical planning for robotic and manual reductions.