Machine Learning Model for Medical Tool Control Precision
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Solution Overview
Problem
Current medical tool control devices, particularly in cardiovascular and cerebrovascular interventions, face challenges in efficiently learning and executing complex operations like guiding wires due to limited adaptation to new tools and reliance on human intuition, leading to reduced precision and increased operator burden.
Innovation Solution
A method involving a processor-trained machine learning model that generates operation commands for medical tool control devices based on blood vessel images, using guide data to update parameters and calculate compensation values for evaluating position accuracy, thereby improving the control device's efficiency and precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained to determine operation commands for medical tool control devices, then the speed and accuracy of medical tool control is improved, but the complexity of the control system increases
Solution Approach 1:
A machine learning model serves as an intermediary between blood vessel images and operation commands, automatically determining optimal guide wire operations. The model processes guide data including destination points, middle target points, and access restriction points to generate compensated evaluation values, replacing manual operator judgment with automated intelligent decision-making.
2Manufacturing precision
If compensation values are calculated and applied to evaluation data based on comparison results, then the precision of reaching destination points is improved, but the computational complexity increases
Solution Approach 1:
Compensation values are pre-calculated based on comparison results between actual positions and guide data before final operation execution. The system determines compensation values for various scenarios (reaching destination points, middle target points, or access restriction points) in advance, allowing the machine learning model to efficiently adjust evaluation values without complex real-time computations during actual operations.
3Reliability
If the machine learning model uses guide data with multiple reference points (destination point, middle target point, access restriction point), then the reliability of medical tool control is improved, but the data processing complexity increases
Solution Approach 1:
The guide data is segmented into multiple reference points with distinct functions: destination points indicate final targets, middle target points represent intermediate milestones, and access restriction points mark constrained areas. This segmentation allows the machine learning model to process different types of spatial information separately and apply appropriate compensation strategies for each point type, improving overall control reliability through structured data organization.
Data Source
AI summary
A method for training, by a processor, a machine learning model for determining an operation of a medical tool control device may comprise the steps of: obtaining an operation command to move a medical tool of the medical tool control device on the basis of the machine learning model from guide data generated using a blood vessel image; generating evaluation data of a position to which the distal end of the medical tool has been moved according to the operation command in a blood vessel image; and updating a parameter of the machine learning model by using the evaluation data, so as to train the machine learning model.