Tissue Discrimination Using Impedance and Multi-Classifier Models
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Solution Overview
Problem
Conventional surgical apparatuses require manual adjustment of energy output based on subjective tissue identification, leading to prolonged surgeries and potential errors due to incorrect energy application.
Innovation Solution
An apparatus and method that measure impedance values unique to each biological tissue using a frequency wave form and input these values into a multi-classifier model, comprising single classifiers and a meta classifier, to automatically adjust energy output for precise tissue discrimination and surgery duration reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of energy output is performed by doctors based on subjective tissue identification, then the surgical apparatus can be operated with simple control, but the surgery duration is prolonged and errors may occur
Solution Approach 1:
The system performs self-service by automatically measuring impedance values, classifying tissue types using machine learning algorithms, and adjusting energy output without requiring manual intervention from the doctor. The surgical apparatus independently completes the entire process of tissue identification and energy parameter optimization.
Solution Approach 2:
The manual mechanical adjustment process is replaced by an automated electronic system that uses impedance measurement and machine learning classification to determine tissue type and automatically set energy parameters, substituting the doctor's subjective visual assessment with objective electrical measurement and algorithmic decision-making.
2Reliability
If energy output is adjusted to match different tissue types, then the reliability of tissue treatment is improved, but the device complexity increases due to multiple classifiers and impedance measurement
Solution Approach 1:
The system introduces an intermediary classification layer between impedance measurement and energy application. The multi-classifier model acts as a mediator that translates raw impedance data into tissue type identification, which then guides energy parameter selection. This intermediary structure improves reliability by ensuring accurate tissue-energy matching.
Solution Approach 2:
The system changes operational parameters (energy output settings) based on measured parameters (impedance values). By measuring electrical impedance characteristics and using these to determine appropriate energy levels, the system dynamically adjusts parameters to match tissue properties, improving treatment reliability.
3Measurement precision
If impedance measurement and multi-classifier model are implemented, then tissue discrimination precision is improved, but the ease of operation is reduced due to automated system requirements
Solution Approach 1:
The automated system performs self-service by independently completing tissue identification and energy parameter optimization without requiring manual intervention from the doctor. The surgical apparatus autonomously measures impedance, classifies tissue types using machine learning algorithms, and adjusts energy output automatically.
Solution Approach 2:
The manual mechanical adjustment process is replaced by an automated electronic system that uses impedance measurement and machine learning classification to determine tissue type and automatically set energy parameters, substituting the doctor's subjective visual assessment with objective electrical measurement and algorithmic decision-making.
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 solution enables accurate tissue discrimination and automatic energy adjustment, reducing surgery duration and preventing errors by leveraging machine learning algorithms and impedance measurement techniques.
Implementation Method 1
an impedance measurer having a first electrode for applying a signal having a frequency wave form to the biological tissue and a second electrode for receiving the signal that passed the biological tissue, and configured to measure an impedance magnitude and an impedance phase according to the frequency wave form of the biological tissue
Data Source
AI summary
The present disclosure relates to an apparatus and method for discriminating biological tissue, and a surgical apparatus using the same, the biological tissue discriminating method being capable of exactly discriminating the biological tissue by measuring an impedance value per frequency, teaching the measured impedance value per frequency in a single classifier according to learning algorithms that are different from one another having the measured impedance value per frequency as an input variable to discriminate the biological tissue, and re-teaching the biological tissue discriminated from each single classifier in a meta classifier to finally discriminate the biological tissue.