Spark-Based Tissue Identification Using Optical and Electrical Signals
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Solution Overview
Problem
Existing methods for tissue detection during surgical procedures face uncertainties that hinder reliable tissue recognition, particularly in distinguishing between benign and malignant tissues.
Innovation Solution
A theragnostics system that combines optical and electrical characteristics of the spark generated between an electrode and tissue, using a surgical station with a generator, detection devices, and a storage and processing unit to determine tissue labels with enhanced precision by integrating spectral and electrical analysis, and includes a self-learning capability for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only optical characteristics (light spectrum) are used for tissue detection, then the system is simpler, but tissue recognition reliability is insufficient
Solution Approach 1:
The patent combines optical detection (light spectrum analysis) and electrical detection (impedance measurement) into a single integrated system. The surgical instrument includes both a light receiver for capturing spark emission spectra and electrical connection for measuring impedance characteristics. This merging of detection modalities resolves the contradiction by achieving reliable tissue recognition through multi-parameter analysis while maintaining a unified device structure rather than separate systems.
Solution Approach 2:
The surgical instrument is designed with multi-functionality, serving both therapeutic (electrosurgical cutting/coagulation) and diagnostic (optical and electrical tissue characterization) purposes. The single instrument performs multiple functions: delivering high-frequency current for surgical effects, generating and detecting light emission for optical analysis, and measuring electrical impedance for tissue differentiation. This universality allows reliable tissue identification without requiring multiple separate devices.
2Measurement precision
If multiple detection methods are combined, then tissue identification precision improves, but device complexity increases
Solution Approach 1:
The patent integrates optical detection (light spectrum analysis) and electrical detection (impedance measurement) into a single unified system. The surgical instrument includes both a light receiver for capturing spark emission spectra and electrical connection for measuring impedance characteristics simultaneously. This merging resolves the contradiction by achieving high-precision tissue identification through multi-parameter analysis while maintaining a consolidated device architecture.
Solution Approach 2:
The system utilizes the surgical spark itself as the source for both therapeutic effect and diagnostic information. The same high-frequency current that performs the surgical cutting or coagulation also generates the light emission and electrical characteristics that are detected for tissue identification. This self-service approach eliminates the need for separate diagnostic probes or additional energy sources, thereby improving measurement precision without proportionally increasing device complexity.
3Productivity
If real-time tissue detection is implemented, then surgical decision-making is improved, but data processing requirements increase
Solution Approach 1:
The system performs preliminary classification of tissue characteristics by comparing detected optical and electrical parameters against pre-stored reference data for different tissue types (benign vs. malignant). This preliminary action enables rapid real-time identification during surgery, improving surgical efficiency by providing immediate feedback without requiring complex post-operative analysis.
Solution Approach 2:
The system implements real-time feedback by continuously monitoring optical characteristics (light spectrum) and electrical characteristics (impedance) during the surgical procedure and immediately providing tissue identification results. This feedback loop allows the surgeon to make informed decisions in real-time about tissue removal boundaries and surgical approach, enhancing productivity through instantaneous diagnostic information without requiring delayed or offline analysis.
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 achieves more reliable and precise tissue identification by utilizing both optical and electrical characteristics, providing real-time feedback and adaptive surgical settings, enabling personalized medicine and improved diagnostic capabilities.
Implementation Method 1
a generator (14) for supplying the instrument (11), in particular its electrode (12), with an electric current suitable for producing the surgical effect, typically a high-frequency current with a frequency above 100 kHz
Implementation Method 2
A spark is maintained between the electrode and biological tissue, from which light is emitted
Implementation Method 3
This light is captured by a light-receiving device and fed to an analysis device that performs a spectral analysis
Implementation Method 4
The spark used to remove the plaques generates light, which is transmitted via an optical fiber to a sensor
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A theragnostics system according to the invention comprises a surgical station (10) and a storage and processing unit (17) which contains a large amount of data in a suitable memory, in which patient data and treatment data, for example in the form of electrical and optical features (E, O), are combined. The electrical features (E) are derived from electrical quantities of voltage and current with which an instrument is powered. The optical features are derived from the light of the spark (15) that is generated when the instrument acts on the tissue (13). By combining electrical features (E) and optical features (O) in a data collection, for example a database, which also contains further features, such as tissue features and optionally patient features, it can be determined automatically with a high degree of certainty whether the instrument is acting on healthy or diseased tissue.The predictive accuracy can be increased by machine learning by adding histological data to the data sets (22) in addition to the electrical features (E) and the optical features (O). In a preferred embodiment, this data is collected in a cloud (26) that is connected to many surgical stations (10). Thus, data acquired at various surgical stations (10) can be collected in the cloud (26).