Endoscopic Energy Output Control for Tissue-Aware Heat Reduction
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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, making it difficult for doctors to perform stable treatments, especially for inexperienced practitioners.
Innovation Solution
A system that utilizes machine learning to analyze endoscope images for tissue type, condition, gripping amount, and tension, and adjusts energy output accordingly through a generator, reducing heat diffusion by autonomously controlling energy devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If doctors manually adjust energy output based on tissue observation, then treatment flexibility is maintained, but treatment stability and consistency deteriorate, especially for inexperienced practitioners
Solution Approach 1:
The system enables autonomous energy output adjustment by having the energy device itself perform the adjustment based on image recognition results. The processor automatically modifies energy output parameters according to detected tissue characteristics, eliminating the need for manual doctor intervention and ensuring consistent treatment delivery.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the image sensor continuously captures tissue images, the processor analyzes tissue characteristics, and the energy output is automatically adjusted based on this real-time feedback. This ensures treatment stability by dynamically adapting energy parameters to actual tissue conditions.
2Productivity
If energy output is increased to improve treatment effectiveness, then treatment efficiency improves, but heat diffusion to surrounding tissues increases causing harmful effects
Solution Approach 1:
The system applies different energy output levels to different tissue types by detecting tissue characteristics through image recognition. The processor determines appropriate energy parameters based on the specific tissue being treated, allowing high energy output for robust tissues while reducing energy for sensitive tissues to prevent heat diffusion damage.
Solution Approach 2:
The system dynamically changes energy output parameters based on real-time tissue detection. The processor adjusts energy frequency, power level, and pulse duration according to the detected tissue characteristics, optimizing treatment efficiency while preventing excessive heat diffusion to surrounding tissues.
3Adaptability or versatility
If manual energy adjustment is used to adapt to different tissue types, then adaptability is maintained, but measurement precision and consistency of tissue characterization deteriorate
Solution Approach 1:
The system replaces manual doctor observation and judgment with an automated image recognition system. The image sensor captures tissue images and the processor automatically analyzes tissue characteristics, providing precise and consistent tissue characterization without relying on individual doctor expertise or subjective assessment.
Solution Approach 2:
The system introduces an intermediary image recognition system between the doctor and the tissue. This intermediary automatically detects and characterizes tissue types through image analysis, providing objective and precise tissue information to guide energy output adjustment without requiring direct manual assessment by the doctor.
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.