Medical Arm Control Using Inverted Learning Model for Surgical Safety
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In medical observation systems, it is challenging to collect a large amount of appropriately labeled data for machine learning, particularly for autonomously operating robot arm devices in endoscopic surgery, due to the unique characteristics of surgical motions, which hinders the efficient construction of learning models.
Innovation Solution
An information processing device and method that generates a learning model by labeling and learning from a large amount of data on operations that should be avoided, using a control unit to autonomously operate a medical arm based on a first learning model, focusing on feature values extracted from state information to prevent undesirable states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning approaches are used with manually labeled surgical motion data, then learning model accuracy can be improved, but data collection efficiency deteriorates due to the difficulty of appropriate labeling
Solution Approach 1:
The patent inverts the conventional approach by training the learning model to predict operations that should be avoided (negative examples) rather than predicting correct operations (positive examples). This inversion makes data collection more efficient because it is easier to identify and label incorrect or unsafe surgical motions than to comprehensively label all correct surgical techniques, thereby resolving the contradiction between model accuracy and data collection efficiency
Solution Approach 2:
The system performs preliminary labeling of surgical motions as 'avoid' or 'not avoid' categories before detailed model training. This preliminary classification creates a structured dataset that accelerates subsequent machine learning processes, improving both data collection efficiency and enabling accurate prediction of operations to avoid during surgery
2Productivity
If comprehensive surgical motion data is collected for machine learning, then learning model construction efficiency can be improved, but data labeling difficulty worsens due to unique characteristics of surgical motions
Solution Approach 1:
The patent addresses the labeling difficulty by inverting the classification task from identifying correct surgical motions to identifying incorrect or unsafe motions. This inversion reduces labeling complexity because unsafe or incorrect motions have more distinguishable characteristics and can be identified more easily than comprehensive correct techniques, thereby improving learning model construction efficiency while reducing labeling difficulty
Solution Approach 2:
The system changes the parameter of classification from positive examples (correct operations) to negative examples (operations to avoid). This parameter change transforms the labeling task into one that is more amenable to automated detection and expert review, reducing the overall difficulty of data labeling while maintaining comprehensive coverage of surgical motions
3Adaptability or versatility
If a learning model is trained to predict all possible surgical operations, then operational completeness can be improved, but system complexity worsens
Solution Approach 1:
The patent extracts and focuses specifically on the critical subset of surgical operations that should be avoided, rather than attempting to model all possible operations. This extraction approach reduces system complexity by concentrating computational resources on predicting only the harmful or incorrect operations, while still maintaining operational completeness for safety-critical functions
Solution Approach 2:
By inverting the prediction target from comprehensive operation prediction to specific negative operation prediction, the system achieves operational completeness for safety purposes without the complexity of modeling all surgical techniques. The inverted approach allows the system to be complete in its safety function while remaining computationally efficient
Data Source
AI summary
There is provided an information processing device (300) including a control unit (324) that controls a medical arm (102) to autonomously operate using a first learning model generated by machine learning a plurality of state information concerning an operation of the medical arm labeled as an operation that should be avoided.