Electrosurgical Energy Control via Machine Learning Prediction
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Solution Overview
Problem
Existing surgical instruments lack effective real-time feedback to ensure adequate vessel sealing, which can lead to incomplete sealing or tissue damage.
Innovation Solution
A computer-implemented method and system that utilize machine learning algorithms, specifically neural networks, to predict the burst pressure probability of a vessel based on data collected from an electrosurgical system. This data includes electrical parameters associated with the delivery of electrosurgical energy, and the system adjusts the energy delivery accordingly to ensure adequate sealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy-based surgical forceps are used to seal vessels without tissue temperature feedback, then the sealing process can be performed efficiently, but the reliability of adequate sealing cannot be ensured
Solution Approach 1:
The patent implements a feedback mechanism by collecting electrical parameter data during energy delivery and using machine learning algorithms to predict burst pressure probability in real-time. This predicted probability serves as feedback to the control system, enabling dynamic adjustment of energy delivery parameters to ensure adequate sealing while maintaining efficiency.
2Reliability
If machine learning algorithms are introduced to predict vessel parameters and control energy delivery, then the reliability of sealing can be improved, but the device complexity increases
Solution Approach 1:
The patent uses machine learning algorithms as an intermediary layer between the energy delivery system and the control system. The algorithms process collected electrical parameter data to predict burst pressure probability, which then informs energy delivery adjustments. This intermediary approach enables intelligent control while managing complexity through automated prediction rather than direct complex control mechanisms.
3Manufacturing precision
If real-time data collection and machine learning prediction are performed during vessel sealing, then the precision of sealing control can be improved, but the loss of time for processing increases
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models using historical surgical data before actual vessel sealing procedures. The models are trained to recognize patterns in electrical parameters that indicate adequate sealing, enabling rapid real-time predictions during surgery without requiring extensive processing time during the critical sealing operation.
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 effectively determines if a vessel is adequately sealed in real-time, allowing for precise control of electrosurgical energy delivery, thereby reducing the risk of incomplete sealing or tissue damage.
Implementation Method 1
delivering electrosurgical energy from the energy source to the vessel to seal the vessel
Data Source
AI summary
A computer-implemented method for controlling delivery of electrosurgical energy to a vessel to seal the vessel, includes collecting data from an electrosurgical system including an instrument and energy source while the instrument is delivering electrosurgical energy from the energy source to a vessel, predicting by using a machine learning algorithm a burst pressure probability of the vessel based on the data, and determining if the vessel is adequately sealed based on the prediction. The data includes an electrical parameter associated with the delivery of the electrosurgical energy. In a case where it is determined that the vessel is not adequately sealed: determining an output by a second machine learning algorithm, communicating the determined output to a computing device associated with the energy source for use in formulating an energy-delivery algorithm, and delivering, using the instrument, additional electrosurgical from the energy source to the vessel to seal the vessel according to the energy-delivery algorithm.