Catheter Robot Training Using Physician Maneuver Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical robotic systems lack sufficient training data to enable hands-free robotic control of catheters, particularly in complex procedures like cardiac catheterization, requiring skilled physician intervention and limiting availability.
Innovation Solution
A training system using sensors on a catheter handpiece and distal end to record physician maneuvers, generating data sets for training a neural network (NN) to guide a robotic arm, enabling precise catheter manipulation based on high-level physician commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual catheter manipulation by skilled physicians is used, then procedural precision and reliability are maintained, but physician availability and productivity are limited
Solution Approach 1:
The system enables self-service automation where the robotic system performs catheter manipulation autonomously based on trained neural network models, eliminating the need for continuous skilled physician intervention while maintaining procedural capabilities
Solution Approach 2:
The system performs preliminary training actions by collecting and processing physician manipulation data to build neural network models, which then enable autonomous operation without requiring real-time skilled intervention during actual procedures
2Productivity
If hands-free robotic control is implemented, then physician availability increases, but sufficient training data and system reliability are lacking
Solution Approach 1:
The system performs preliminary data collection and model training before autonomous operation, gathering extensive physician manipulation data and processing it through neural networks to ensure sufficient training coverage for reliable hands-free control
Solution Approach 2:
The system implements feedback mechanisms where physician manipulation data is continuously collected, processed to improve neural network models, and used to enhance system reliability through iterative learning and validation
3Measurement precision
If neural network training with extensive data collection is performed, then system precision and adaptability improve, but system complexity and training time increase
Solution Approach 1:
The training system is segmented into modular components including data collection modules, processing modules, and validation modules, allowing systematic handling of complex training tasks while maintaining manageable system architecture
Solution Approach 2:
The system performs preliminary data preprocessing and feature extraction before main training, organizing extensive physician manipulation data into structured formats that reduce training complexity while maintaining positioning accuracy
Data Source
AI summary
Methods and systems are provided for training machine learning (e.g., NN) or other artificial intelligence (AI) models to control a robot arm to manipulate a catheter to robotically perform an invasive clinical catheter-based procedure.