RF Spark Tissue Identification Using Optical-Electrical Fusion
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Solution Overview
Problem
Existing tissue recognition methods during surgical interventions face uncertainties, making reliable differentiation between benign and malignant tissues difficult due to inconsistencies in analyzing light emitted from radio frequency sparks.
Innovation Solution
A theragnostic system that combines optical and electrical features from radio frequency sparks, utilizing a surgical station with a generator, detection devices, and a storage and processing unit to enhance tissue identification accuracy by integrating spectral analysis with electrical parameters, enabling self-learning and cloud-based data analysis for improved tissue characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only optical features from spark light are used for tissue recognition, then the system is simpler, but tissue identification reliability is insufficient
Solution Approach 1:
The patent combines optical features (from spark light spectral analysis) and electrical features (from RF current/voltage measurements) into a unified tissue recognition system. The storage and processing device integrates both feature types to determine tissue labels, thereby improving identification reliability while managing system complexity through coordinated multi-source data fusion.
2Measurement precision
If spectral analysis is performed on spark light to identify tissue, then tissue differentiation is possible, but uncertainties remain that make reliable recognition difficult
Solution Approach 1:
The system incorporates feedback mechanisms where the storage and processing device continuously refines tissue identification by comparing detected optical and electrical features against stored reference data. The system adjusts and optimizes its recognition algorithms based on accumulated surgical data, thereby reducing uncertainties and improving both measurement precision and recognition reliability over time.
3Measurement precision
If multiple data sources are integrated for tissue analysis, then identification accuracy improves, but data processing complexity increases
Solution Approach 1:
The storage and processing device is designed as a multi-functional unit that simultaneously handles optical spectrum analysis, electrical parameter measurement, data storage, pattern recognition, and tissue classification. This universal device consolidates multiple processing functions into a single system, improving tissue identification accuracy while managing overall data processing complexity through integrated architecture.
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 system provides more reliable and accurate tissue differentiation by leveraging both optical and electrical features, allowing for precise identification of benign or malignant tissues, even with low-quality spectra, and supports personalized medicine through continuous data input and machine learning.
Implementation Method 1
a generator for supply of the instrument, particularly its electrode, with an electrical current suitable for producing the surgical effect, typically a radio frequency current having a frequency above 100 kHz
Implementation Method 2
A spark from which light originates is maintained between the electrode and biological tissue. This light is received by means of a light receiving device and is supplied to an analysis device that carries out a spectral analysis.
Implementation Method 3
an analysis device that carries out a spectral analysis. From the created spectrum, optionally also by means of pattern recognition and by means of comparison with light features stored in the data base, it can be concluded whether the tissue is harmless, benign tissue or degenerated, malign tissue.
Data Source
AI summary
A theragnostic system includes a surgical station and a storage and processing device that contains data in a suitable storage in which patient data and treatment data, e.g. in form of electrical and optical features, are combined. The electrical features are derived from electrical parameters of the voltage and the current with which an instrument is supplied. The optical features are derived from light of the spark that is produced upon influencing the tissue. By combining electrical and optical features in a data collection, that even contains additional features, such as tissue features and patient characteristics, it can be determined whether the instrument influences benign or malign tissue. The prediction accuracy can be increased by machine learning by adding histological data to the data sets. These data can be collected in a cloud computing system that is connected with many surgical stations.