A Raman spectroscopy bio-sensor for tissue discrimination in surgical robotics

Area of Science:

• Biomedical Engineering
• Surgical Technology
• Spectroscopy

Background:

• Accurate tumor margin identification is crucial in endoluminal robotic surgery.
• Ambiguous tissue margins pose challenges for surgeons during robot-assisted procedures.
• Existing methods may lack real-time, specific tissue differentiation.

Purpose of the Study:

• To develop a fiber-based Raman sensor for real-time tumor margin identification.
• To adapt the sensor for the ARAKNES (Array of Robots Augmenting the KiNematics of Endoluminal Surgery) robotic platform.
• To create a user-friendly system for classifying tissue types during surgery.

Main Methods:

• Development of a fiber-based Raman sensor with a disposable sterile sleeve.
• Integration of the sensor with the ARAKNES robotic platform.
• Implementation of a supervised multivariate classification algorithm for tissue analysis based on Raman fingerprints.
• Testing on excised tissue for classification accuracy.

Main Results:

• The developed Raman sensor successfully identified different tissue types.
• The user-compatible interface allowed classification without prior spectroscopic knowledge.
• The system demonstrated potential for intraoperative use in identifying ambiguous margins.
• The protocol minimized inter-patient data variability.

Conclusions:

• The fiber-based Raman sensor is a viable tool for tumor margin identification in endoluminal robotic surgery.
• The system offers a user-friendly approach to tissue classification, enhancing surgical precision.
• This technology has the potential to improve patient outcomes by ensuring complete tumor removal.