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

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.