Assistance Device for Energy Treatment Automation Reliability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing energy devices for treating living tissue lack effective methods for controlling energy application, particularly when the estimation accuracy of intraoperative information by trained models is low.
Innovation Solution
An assistance device equipped with a processor that acquires electrical information related to an energy signal, inputs it to a trained model to estimate intraoperative information, and performs control only when the accuracy meets a fixed value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If trained model estimation is used to control energy device, then automation extent is improved, but reliability deteriorates when estimation accuracy is low
Solution Approach 1:
The system implements feedback by acquiring actual intraoperative information during treatment and comparing it with the estimated intraoperative information from the trained model. Based on this comparison, the system determines whether to trust the model's estimation for control decisions, creating a closed-loop feedback mechanism that adjusts automation reliability based on real-time performance validation.
Solution Approach 2:
The system applies partial automation by selectively using the trained model's estimation only when accuracy requirements are met. When estimation accuracy is insufficient, the system reduces automation level and relies more on actual measured data, implementing a nuanced approach that applies automation partially rather than fully or excessively.
2Measurement precision
If multiple data sources are integrated for estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple data sources including electrical information from the energy device, optical information from imaging systems, and actual intraoperative measurements into a unified estimation framework. This combination of diverse data types enhances the precision of intraoperative information estimation by cross-validating signals and reducing uncertainty through multi-modal data integration.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An assistance device includes: a processor, the processor being configured to acquire electrical information related to an energy signal for driving an energy device configured to apply energy to a site to perform treatment on the site, input the electrical information to a trained model to cause the trained model to estimate intraoperative information that is related to the site indicated during an operation, determine whether or not accuracy of the intraoperative information is equal to or greater than a fixed value, and perform control according to the intraoperative information when it is determined that the accuracy is equal to or greater than the fixed value.