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Computer vision-guided rapid and precise automated cranial microsurgeries in mice
Zahra S Navabi1, Ryan Peters1,2, Beatrice Gulner1
1Department of Mechanical Engineering, University of Minnesota, Twin Cities, MN, USA.
Science Advances
|April 9, 2025
Summary
A novel robotic system uses computer vision and machine learning to perform precise cranial surgeries (craniotomies). This automated approach significantly reduces surgery time and enhances accuracy for brain interfacing procedures.
Area of Science:
- Neurosurgery
- Robotics
- Medical Imaging
Background:
- Cranial microsurgery, including craniotomies, is essential for brain interfacing but demands high skill, time, and precision.
- Current methods risk damage to the brain and dura mater due to the complexity of skull anatomy.
Purpose of the Study:
- To develop and evaluate a computer vision-guided robotic system for automated, precise craniotomies.
- To reduce surgical time and improve accuracy in cranial microsurgery.
Main Methods:
- A computer vision-guided craniotomy robot (CV-Craniobot) was developed, utilizing machine learning to analyze optical coherence tomography (OCT) images.
- The system accurately estimates dorsal skull anatomy and guides a robotic mill for precise bone removal.
Main Results:
- The CV-Craniobot achieved near 100% success rates for small (2-4 mm diameter) craniotomies within 2 minutes.
- Large craniotomies covering the dorsal cortex were completed in under 10 minutes.
Conclusions:
- The CV-Craniobot enables rapid and precise craniotomies, significantly outperforming human practitioners in speed.
- This technology reduces the need for extensive surgical training and minimizes risks associated with manual craniotomies.
