Rice University Trustee Professor of Mechanical Engineering and Bioengineering Benjamin Fregly [Photo courtesy of Rice University]
A research team led by Rice University engineer Benjamin Fregly has developed new software that allows clinicians and researchers to design personalized treatments for patients with movement impairments, using digital models that simulate individual anatomy and physiology.
The Neuromusculoskeletal Modeling (NMSM) Pipeline software enables the creation of “digital twins,” or patient-specific computer models, to test and optimize treatments such as orthopedic surgery, physical therapy and neurorehabilitation before they are applied in real life. The work was recently published in the Journal of NeuroEngineering and Rehabilitation.
“A significant reason for suboptimal functional recovery is that existing treatment design methods do not adequately address the unique clinical situation and specific needs of each patient,” said Fregly — the Trustee Professor of Mechanical Engineering and Bioengineering and a Cancer Prevention and Research Institute of Texas Scholar in the George R. Brown School of Engineering and Computing at Rice — in a news release. “Our goal was to create a cutting-edge physics- and physiology-based computational tool that would model each patient’s unique anatomy and physiology with sufficient accuracy to predict the patient’s post-treatment movement function with high reliability.”
Movement disorders caused by conditions such as stroke, osteoarthritis, Parkinson’s disease and spinal cord injury affect approximately 1.7 billion people worldwide, including nearly one in five U.S. adults. These impairments often lead to reduced independence, lower quality of life and higher healthcare costs. According to the researchers, current treatment planning approaches do not always yield satisfactory outcomes for patients.
The NMSM Pipeline builds on OpenSim, an existing open-source musculoskeletal modeling tool developed at Stanford University, and introduces two major components: a model personalization toolset and a treatment optimization toolset. Together, they allow users to simulate how changes to anatomy, neural control or external devices would affect movement function after treatment.
In one example, the team revisited a rehabilitation approach originally developed in 2007 for a patient with medial knee osteoarthritis. The researchers used the new software to replicate the outcome of an invasive orthopedic surgery with a walking modification alone.
Unlike machine learning approaches, the physics-based models used in the pipeline can accurately predict novel treatment outcomes with less input data and no need for extensive training datasets.
A digital twin can be created in as little as one day with basic patient movement data. While full treatment simulations take longer, the researchers believe that developing clinical best practices could reduce the entire process to a few days. The software runs on MATLAB and is designed to require no custom programming.
“We are excited about the NMSM Pipeline’s potential to transform the treatment design landscape for movement impairments,” Fregly said. “We hope that by making high-end neuromusculoskeletal modeling, simulation and optimization capabilities easy to use, computationally fast and freely available, the medical and research communities will begin to explore how objective predictions of a patient’s post-treatment movement function based on personalized models can augment — and hopefully improve — subjective predictions based on clinical experience.”
The project received funding from the National Institutes of Health, the Cancer Prevention and Research Institute of Texas and the National Science Foundation.
