Endoscopic Image-Guided Energy Output Control for Tissue Protection
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Solution Overview
Problem
Existing surgical energy devices face challenges in accurately adjusting energy output to prevent heat diffusion to surrounding tissues, which is influenced by tissue type, condition, gripping amount, and tension, requiring experienced manual adjustments that can be difficult for non-experts.
Innovation Solution
A system utilizing machine learning models to analyze endoscope images for tissue type, condition, gripping amount, and tension, and autonomously adjust energy output to minimize heat diffusion, including a controller, generator, and energy device that integrates image recognition and energy control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual energy output adjustment is used by non-expert operators, then ease of operation is maintained, but manufacturing precision (energy output accuracy) deteriorates
Solution Approach 1:
The system enables self-service operation where the energy device automatically adjusts its own energy output based on real-time image analysis of tissue characteristics. The control unit autonomously processes images from the imaging unit, determines tissue type and condition, and modifies energy output without requiring expert manual intervention, thus maintaining ease of operation while improving precision.
Solution Approach 2:
The patent replaces the mechanical/manual adjustment system with an automated image-based control system. Instead of relying on operator skill to manually adjust energy output, the system uses an imaging unit to capture tissue images, a control unit to analyze these images and determine tissue characteristics, and automatically adjusts energy output based on this analysis, substituting human judgment with automated optical-electronic control.
2Manufacturing precision
If automated image-based energy output adjustment is implemented, then manufacturing precision (energy output accuracy) is improved, but device complexity increases
Solution Approach 1:
The control unit serves multiple functions: it controls the imaging unit to capture tissue images, processes these images to determine tissue type and condition, and adjusts the energy output based on this analysis. By integrating these multiple functions into a single control unit, the system achieves high energy output accuracy without proportionally increasing overall device complexity.
Solution Approach 2:
The patent merges the imaging unit and energy output control into an integrated system where the control unit coordinates both functions. The imaging unit and energy delivery system are combined under unified control, allowing the system to use image data directly for energy adjustment without requiring separate complex control mechanisms, thus improving precision while managing complexity.
3Manufacturing precision
If real-time image analysis is performed to optimize energy output, then manufacturing precision (energy output accuracy) is improved, but loss of time (processing time) increases
Solution Approach 1:
The system performs continuous real-time image analysis during the energy delivery process. The imaging unit continuously captures tissue images, and the control unit continuously processes these images to monitor tissue characteristics and adjust energy output dynamically throughout the procedure. This continuous operation eliminates interruptions and maintains both high precision and efficiency.
Solution Approach 2:
The control unit is pre-programmed with algorithms and criteria for analyzing tissue images and determining appropriate energy output settings. By preparing the analysis framework in advance, the system can quickly process images during operation without requiring complex real-time decision-making, thus achieving high energy output accuracy with minimal processing time delay.
Data Source
AI summary
The system includes a memory that stores first and second trained models, and a processor. The processor acquires a captured image in which at least one energy device and at least one biological tissue are imaged. The processor detects a bounding box from the captured image by processing based on the first trained model and estimates the image recognition information from the captured image in the bounding box by processing based on the second trained model. The processor outputs an energy output adjustment instruction based on the estimated image recognition information to the generator. The generator controls the energy supply amount to the energy device based on the energy output adjustment instruction.