Computer Vision Control of Electrosurgical Energy Generator
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Solution Overview
Problem
Current electrosurgical systems lack effective automated control algorithms to prevent tissue injury during procedures, particularly in laparoscopic surgery, due to limited visibility and potential misuse, as they rely on voltage and current-based closed-loop control schemes that do not prevent activation beyond the field of view or handle large tissue bundles.
Innovation Solution
An energy-based surgical system that utilizes machine vision and machine learning to capture image data from an endoscope, determine tissue thickness, conductivity, and type, and adjust energy output accordingly, with markers on the electrosurgical instrument to enhance precision and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voltage and current-based closed-loop control schemes are used, then energy delivery control is achieved, but prevention of activation beyond field of view and misuse conditions is not possible
Solution Approach 1:
The patent introduces computer vision technology and machine learning algorithms as intermediary systems between the surgeon's activation command and the energy delivery. The vision system captures images, detects tissue characteristics, and provides contextual information to the control algorithm, which then adjusts energy delivery parameters. This intermediary layer enables prevention of misuse conditions by verifying proper tissue engagement before allowing energy activation.
Solution Approach 2:
The system implements a feedback loop where the control algorithm continuously monitors visual feedback from the camera, analyzes tissue characteristics, and adjusts energy delivery in real-time. The system provides feedback to the surgeon about detected tissue properties and activation readiness, enabling dynamic adaptation to prevent inadvertent injury while maintaining safe energy delivery when conditions are appropriate.
2Adaptability or versatility
If automated control algorithms utilizing computer vision and machine learning are implemented, then context-aware control and prevention of misuse are achieved, but device complexity increases
Solution Approach 1:
The control algorithm is designed to perform multiple functions: detecting tissue characteristics, determining activation readiness, preventing misuse conditions, and adjusting energy parameters. The vision system serves multiple purposes including tissue identification, thickness measurement, and verification of proper instrument-tissue engagement. This multi-functional approach consolidates what could be separate systems into a unified control architecture.
Solution Approach 2:
The machine learning model automatically learns and adapts to different tissue types and surgical conditions without requiring manual programming for each scenario. The system self-calibrates by processing visual data and making real-time decisions about safe activation parameters, reducing the need for complex pre-programming and manual intervention while maintaining high adaptability.
3Measurement precision
If vision-based tissue characterization is used, then precise energy delivery control is achieved, but processing time and computational requirements increase
Solution Approach 1:
The machine learning model is pre-trained on extensive datasets of tissue images and characteristics before deployment. This preliminary training allows the model to quickly recognize and characterize tissue types during surgery without requiring real-time complex computations. The system performs preliminary assessment of tissue properties as images are captured, enabling rapid decision-making about safe energy parameters.
Solution Approach 2:
The vision system captures continuous video feed and processes key frames rather than analyzing every pixel of every frame. The machine learning model focuses on extracting the most relevant tissue characteristics needed for safe energy delivery, such as tissue type, thickness, and vascularization, rather than attempting to analyze all visual information. This selective processing reduces computational burden while maintaining measurement precision.
Data Source
AI summary
An electrosurgical instrument includes a pair of jaws, an endoscope configured to capture image data of a surgical site including the electrosurgical instrument, and an energy generator coupled to the electrosurgical instrument. The energy generator is configured to generate an energy output to the electrosurgical instrument. The system also includes an endoscope controller which includes a processor and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the energy-based surgical system to access the captured image, determine a thickness of tissue grasped by the pair of jaws based on the image, and control the energy output of the energy generator based on the determined thickness.