Surgical Device Control Range Adaptation for Safe Input Blocking
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Solution Overview
Problem
Incorporating non-traditional algorithms, such as machine learning, into medical technology for surgical devices is challenging due to the time-consuming nature of training and the inconvenience of data aggregation and processing.
Innovation Solution
A device uses a machine learning model to determine an allowable operation range for surgical devices, reducing training iterations and improving data processing efficiency by adaptive learning algorithms, which identify safe control input ranges based on surgical operation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used to determine allowable operation ranges for surgical devices, then the precision and safety of device control is improved, but the training time and data processing complexity increases
Solution Approach 1:
The patent performs data aggregation, cleaning, and preprocessing in advance before training the machine learning model. By preparing the training data beforehand and organizing it into structured formats, the actual model training time is reduced while maintaining high precision in determining allowable operation ranges.
Solution Approach 2:
The patent extracts only the most relevant features and parameters from surgical device data for machine learning training. By selecting and extracting key variables that most impact safety and precision, the training process becomes more efficient without sacrificing the accuracy of allowable operation range determination.
2Reliability
If machine learning models are trained to improve surgical device control, then the reliability of device operation is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent divides the machine learning system into modular components: data aggregation modules, preprocessing modules, model training modules, and deployment modules. Each module handles specific tasks independently, making the overall complex system more manageable and easier to validate for reliability in surgical device control.
Solution Approach 2:
The patent introduces intermediate layers between raw surgical device data and the machine learning model, including data cleaning, feature extraction, and validation layers. These intermediaries simplify the input data structure and reduce the complexity burden on the core machine learning model while maintaining reliable operation range determination.
3Productivity
If adaptive learning algorithms are used to process surgical data streams, then the productivity of data analysis is improved, but the computational resources and system complexity increase
Solution Approach 1:
The patent implements periodic batch processing of surgical device data streams, where data is aggregated and processed in structured intervals rather than continuously. This periodic approach improves computational efficiency and productivity by allowing optimized batch operations while reducing the immediate computational burden on the system.
Solution Approach 2:
The patent dynamically adjusts processing parameters such as batch size, sampling rates, and model complexity based on available computational resources and data characteristics. By changing these parameters adaptively, the system maintains high data processing productivity while managing computational resource requirements and system complexity.
Data Source
AI summary
A device, such as a computing device or a surgical device, may receive surgical operation data associated with a surgical operation. Based on the surgical operation data, a device may identify a surgical device to be used for a surgical operation and/or one or more surgical steps associated with a surgical operation. Based on the identified surgical device, the one or more surgical steps, and/or the surgical operation data, a device may determine an allowable operation range controlling a surgical device for a surgical procedure. A device may receive an adjustment input configuration to control a surgical device for a surgical step. A device may determine whether the adjustment input configuration is outside of the determined allowable operation range. Based on a determination that the adjustment input configuration is outside of the determined allowable operation range, a device may block the adjustment input configuration to control a surgical device.