AI Learning System for Energy-Based Surgical Tissue Sealing
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Solution Overview
Problem
The effectiveness of energy-based surgical procedures, such as vessel sealing, heavily depends on the clinician's experience, leading to variability in outcomes and a need for improved control over energy application to ensure proper tissue sealing without damage.
Innovation Solution
An artificial-intelligence learning system is integrated into energy-based surgical systems to process tissue images and control parameter values, providing adjusted settings and indications to clinicians based on predicted outcomes, using convolutional neural networks and clinician feedback for training and data tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy-based surgical instruments are used to treat tissue, then effective tissue treatment is achieved, but outcomes vary depending on clinician experience
Solution Approach 1:
The system performs self-diagnosis and self-adjustment by automatically analyzing tissue images, predicting optimal energy parameters, and adjusting control settings without requiring manual intervention or extensive clinician expertise. The artificial intelligence learning system enables the device to serve itself in determining treatment parameters.
Solution Approach 2:
The patent replaces manual clinician judgment and experience-based decision-making with an artificial intelligence learning system that processes tissue images and automatically determines optimal energy delivery parameters. This substitution of mechanical/human decision processes with intelligent algorithms ensures consistent outcomes independent of operator skill level.
2Manufacturing precision
If manual control of energy parameters is used, then flexibility in treatment is maintained, but precision and consistency of outcomes are reduced
Solution Approach 1:
The system captures tissue images during the procedure, processes them through the artificial intelligence learning system, receives feedback on treatment outcomes, and automatically adjusts energy delivery parameters accordingly. This closed-loop feedback mechanism ensures precise and consistent energy application while adapting to real-time tissue responses.
Solution Approach 2:
The artificial intelligence learning system dynamically adjusts multiple control parameters including energy level, pulse duration, and delivery pattern based on real-time tissue image analysis. This automated parameter optimization achieves precise energy delivery while the system manages the complexity of coordinating multiple parameter changes.
Data Source
AI summary
The present disclosure relates to energy-based surgical procedures. In accordance with aspects of the present disclosure, a computer implemented method includes accessing an image of tissue of a patient, accessing control parameter values of a generator configured to provide energy based on control parameters, processing the image of the tissue and the control parameter values by an artificial-intelligence learning system to provide an output relating to configuration of the control parameters, providing an indication to a clinician based on the output where the indication indicates whether to maintain the control parameter values, and providing adjusted control parameter values for the generator based on the output of the artificial-intelligence learning system if the indication indicates not to maintain the control parameter values.