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Tissue-probe contact assessment during robotic surgery using single-fiber reflectance spectroscopy
Lotte M de Roode1,2, Lisanne L de Boer1, Henricus J C M Sterenborg1
1Image-Guided Surgery, Department of Surgery, the Netherlands Cancer Institute-Antoni van Leeuwenhoek, Plesmanlaan 121, Postbus 90203, 1066 CX Amsterdam, The Netherlands.
Biomedical Optics Express
|December 16, 2024
Summary
Single Fiber Reflectance (SFR) with machine learning accurately assesses robotic surgery probe-tissue contact. This technique enhances optical measurements during minimally invasive cancer surgery by confirming direct contact.
Area of Science:
- Surgical Oncology
- Biomedical Optics
- Robotic Surgery
Background:
- Robotic surgery enhances minimally invasive procedures in surgical oncology.
- Optical techniques can differentiate cancerous from healthy tissue.
- Direct tissue contact is often required for reliable optical measurements, posing a challenge in robotic surgery due to lack of tactile feedback.
Purpose of the Study:
- To investigate the use of Single Fiber Reflectance (SFR) for assessing tissue-probe contact in robotic surgery.
- To develop a machine learning algorithm for classifying tissue-probe contact during optical measurements.
Main Methods:
- Utilized Single Fiber Reflectance (SFR) to analyze optical properties of tissue.
- Developed and applied a machine learning-based algorithm to classify direct tissue-probe contact.
- Conducted experiments in an ex-vivo tissue setup.
Main Results:
- Achieved an average accuracy of 93.9% in classifying probe-tissue contact using the developed algorithm.
- Demonstrated the effectiveness of SFR in determining adequate tissue-probe contact.
- Validated the machine learning approach for contact assessment.
Conclusions:
- Single Fiber Reflectance (SFR) combined with machine learning is a reliable method for assessing tissue-probe contact in robotic surgery.
- This technique can overcome the challenge of lacking tactile feedback in robotic procedures.
- The findings suggest potential for in vivo clinical application to improve optical measurements in robotic surgery.
