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A Surgeon Specific Automatic Path Planning Algorithm for Deep Brain Stimulation
Yuan Liu1, Benoit M Dawant1, Srivatsan Pallavaram1
1Dept. of Electrical Eng. and Comp. Science, Vanderbilt University, Nashville, TN 37235, USA.
Proceedings of Spie--The International Society for Optical Engineering
|December 26, 2025
Summary
This study developed a computer-assisted trajectory planning system for deep brain stimulation surgery. The system mimics surgeon preferences, showing potential for safer and standardized neurological treatments.
Area of Science:
- Neurosurgery
- Medical Robotics
- Computational Neuroscience
Background:
- Deep brain stimulation (DBS) requires precise electrode placement for treating neurological disorders.
- Safe DBS surgery necessitates avoiding critical brain structures during trajectory planning.
- Current automated trajectory planning lacks consensus among neurosurgeons regarding optimal parameters.
Purpose of the Study:
- To develop a trajectory planning system that learns and mimics individual neurosurgeon preferences.
- To create a foundation for standardizing surgical approaches in DBS through learned parameters.
- To validate an approach for computer-assisted path planning in DBS surgery.
Main Methods:
- Developed a trajectory planning algorithm incorporating parameters for individual surgeon preferences.
- Designed the system to potentially learn these parameters automatically in the future.
- Conducted a blinded study where two neurosurgeons compared manual and computed trajectories.
Main Results:
- Neurosurgeons preferred the computed trajectory over their manual one in 10 out of 40 cases.
- The computed trajectory was deemed equivalent or acceptable in 27 of the remaining cases.
- Demonstrated the clinical utility of computer-assisted path planning in DBS.
Conclusions:
- The developed trajectory planning approach can effectively mimic individual surgeon preferences.
- Computer-assisted path planning shows significant potential for improving DBS surgery safety and standardization.
- This work represents a crucial first step towards intelligent, personalized surgical planning systems.
