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Tissue-probe contact assessment during robotic surgery using single-fiber reflectance spectroscopy.

Lotte M de Roode^1,2, 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.

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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.