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414
Statistical Surgical Process Modeling: Analysis of Workflow and Performance of Emerging Technologies in Image-Guided
Anshuj Deva1,2, Tatiana A Rypinski1,2, Parvathy Sudhir Pillai1,2
1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Annals of Biomedical Engineering
|September 30, 2025
Summary
Statistical surgical process modeling (SPM) quantifies benefits of new spine surgery technologies. Intraoperative imaging and 3D navigation significantly improve efficiency and accuracy, reducing procedure times and errors.
Area of Science:
- Medical Informatics
- Surgical Engineering
- Computational Biology
Background:
- Surgical process modeling (SPM) offers a framework for understanding complex surgical workflows.
- SPM aids in evaluating emerging technologies' potential benefits, particularly in image-guided spine surgery.
Purpose of the Study:
- Extend SPM ontology into a statistical framework for computational modeling and simulation.
- Analyze the impact of new spine surgery technologies on quantitative outcomes using two use cases.
Main Methods:
- Mapped SPM ontology (phase, step, activity) to a directed graph.
- Described activities using statistical distributions, parameterized and validated via studies.
- Applied the framework to evaluate long-length imaging and intraoperative CT/3D navigation in spine surgery.
Main Results:
- Statistical SPMs revealed benefits of intraoperative long-length imaging, reducing median cycle time for target localization.
- 3D navigation for pedicle screw placement decreased median procedure time and improved geometric accuracy.
- Emerging technologies demonstrated reduced outliers and improved quantitative outcomes compared to conventional methods.
Conclusions:
- Statistical SPMs are valuable for evaluating and communicating complex surgical workflows and technology benefits.
- The developed SPM framework supports virtual clinical trials for assessing emerging surgical technologies.
- This methodology provides insights into outcome measures difficult to assess through traditional research methods.
