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Published on: October 17, 2016
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.
Abstract:
The introduction of robotic surgery has improved minimally invasive surgery, and now robotic surgery is used in several areas of surgical oncology. Several optical techniques can be used to discriminate cancer from healthy tissue based on their optical properties. These technologies can also be employed with a small fiber-optic probe during minimally invasive surgery; however, for acquiring reliable measurements, some optical techniques require the fiber-optic probe to be in direct contact with the tissue. The lack of tactile feedback in robotic surgery makes assessing tissue-probe contact suitable for optical contact measurements challenging for the surgeon. In this study, we investigated the use of single fiber reflectance (SFR) to determine tissue-probe contact adequately. A machine learning-based algorithm was developed to classify if direct tissue-probe contact was present during the measurement in an ex-vivo tissue setup. Using this classification algorithm, an average accuracy of 93.9% was achieved for assessing probe-tissue contact, suggesting that this technique can be utilized to assess tissue-probe contact in an in vivo clinical setting.
Insights
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.
