Surgical Instrument Tissue Identification via Impedance Sensing
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Solution Overview
Problem
Surgical instruments, such as energy-based surgical forceps, rely heavily on surgeon experience and visualization to differentiate between tissue types for treatment, often risking unintended damage to non-target tissues like nerves during procedures.
Innovation Solution
A surgical system incorporating a machine learning application that uses impedance and power sensing data to determine the type of tissue grasped by the instrument, communicating this information to a controller to either continue or inhibit energy delivery, thereby preventing inappropriate tissue treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgeons rely on experience and visualization to distinguish tissue types, then surgical judgment is achieved, but the risk of treating inappropriate tissues increases
Solution Approach 1:
The patent replaces the mechanical/visual inspection system with an electrical measurement system. Impedance sensing electrodes measure electrical properties of tissue to automatically identify tissue type, substituting surgeon visual judgment with objective electrical measurements. This resolves the contradiction by providing more reliable tissue identification that reduces the risk of treating inappropriate tissues.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring impedance characteristics during tissue treatment and comparing them against stored profiles. When impedance patterns indicate inappropriate tissue type, the system provides feedback to alert the surgeon or automatically adjust treatment parameters, thereby reducing the risk of treating wrong tissues while maintaining treatment effectiveness.
2Productivity
If energy-based surgical forceps are used to treat tissue, then effective tissue treatment is achieved, but unintended damage to non-target tissues may occur
Solution Approach 1:
The system performs preliminary tissue identification by measuring impedance characteristics before applying full energy treatment. The impedance profile is analyzed to determine tissue type, and only after confirming appropriate tissue type does the system proceed with energy delivery. This preliminary action prevents unintended damage while maintaining treatment efficiency.
Solution Approach 2:
Real-time impedance monitoring provides continuous feedback during energy delivery. If impedance changes indicate the presence of non-target tissues, the system can automatically reduce or terminate energy delivery, preventing unintended damage while allowing efficient treatment of appropriate tissues.
3Measurement precision
If machine learning applications are used to determine tissue type, then tissue identification accuracy is improved, but device complexity increases
Solution Approach 1:
The generator performs multiple functions: it delivers energy for tissue treatment, senses impedance through the same electrical leads, and runs machine learning algorithms for tissue identification. This multi-functionality reduces overall system complexity by eliminating separate dedicated sensing devices while maintaining high measurement precision.
Solution Approach 2:
The system uses its own operational data (electrical leads and impedance measurements taken during normal treatment) to perform tissue identification without requiring external or additional sensing equipment. The machine learning application analyzes data already being collected during treatment, making the system self-sufficient and reducing complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively reduces the risk of treating inappropriate tissues by determining their type within the initial 250 ms of treatment, preventing permanent damage and providing a warning for non-target tissues, thus enhancing surgical precision and safety.
Implementation Method 1
sensor circuitry electrically coupled to the first and second electrical leads and configured to sense impedance and/or power during the conduction of energy between the electrically-conductive plates and through tissue grasped between the first and second jaw members
Data Source
AI summary
A surgical system includes an end effector assembly having first and second jaw members, a generator, and one or more machine learning applications. The first and/or second jaw member is movable relative to the other from a spaced-apart position to an approximated position for grasping tissue therebetween. The jaw members are configured to conduct energy therebetween and through tissue grasped therebetween. The generator includes an energy output configured to supply energy to the jaw members, a main controller configured to control the energy output, and sensor circuitry configured to sense impedance and/or power. The machine learning application(s) is configured to determine a type of tissue grasped between the first and second jaw members based upon the impedance and/or power sensed by the sensor circuitry.