Perceptual Surgical Knife with Wavelet Denoising.

Area of Science:

• Medical Technology
• Biomedical Engineering
• Sensing Technology

Background:

• Robotic surgery is rapidly advancing in medical applications.
• The surgical knife is critical for the success of robotic surgery.
• Detecting subtle signals during surgery is challenging due to noise.

Purpose of the Study:

• To propose a novel perceptual surgical knife based on field-effect transistor (FET) principles.
• To enhance the detection of electrons or electric fields from the human body during robotic surgery.
• To address signal interference from high-frequency Gaussian white noise.

Main Methods:

• Utilizing field-effect transistor (FET) principles for signal detection.
• Implementing a wavelet denoising approach to mitigate high-frequency noise.
• Developing a perceptual surgical knife for enhanced intraoperative sensing.

Main Results:

• The proposed perceptual surgical knife effectively detects human body electric fields with distinguishable signals.
• Wavelet denoising significantly reduces high-frequency noise, improving motion discrimination.
• The system demonstrates high sensitivity, low cost, and good repeatability.

Conclusions:

• The integration of FET-based sensing and wavelet denoising offers a promising solution for advanced robotic surgery.
• This perceptual surgical knife enhances surgical precision and safety.
• The technology is characterized by its high sensitivity, cost-effectiveness, and repeatability.