Machine Learning Control of Surgical Devices for Knife End Stops
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Solution Overview
Problem
Conventional linear clamping, cutting, and stapling devices require two hands for operation and lack intelligent algorithms to accurately evaluate staple formation and knife movement, leading to inefficiencies and potential malfunctions.
Innovation Solution
A computer-implemented method using machine learning classifiers to analyze sensor data from surgical devices, predicting end stop points and ensuring proper staple formation and knife movement through filtering, generating difference and zero-crossing data, and controlling motors based on probability predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are implemented to predict end stop points and evaluate staple formation, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system continuously monitors sensor data during the surgical procedure and feeds this information back to the machine learning classifier, which adjusts predictions in real-time based on actual tissue response and device performance, improving measurement precision through iterative feedback loops
Solution Approach 2:
The patent replaces traditional mechanical sensing and control mechanisms with machine learning-based predictive algorithms that process sensor data to determine end stop points, substituting complex mechanical evaluation systems with intelligent software-based solutions
2Reliability
If intelligent algorithms are added to evaluate staple formation and knife movement, then reliability is improved, but device complexity increases
Solution Approach 1:
The machine learning classifier autonomously evaluates sensor data to predict end stop points and assess staple formation quality without requiring external intervention, enabling the system to self-monitor and self-adjust for optimal performance and safety
Solution Approach 2:
Real-time feedback from sensors monitoring knife position, tissue impedance, and actuator force is continuously fed to the intelligent algorithms, which adjust surgical parameters dynamically to maintain reliability and prevent malfunctions
3Measurement precision
If sensor data is processed through filtering and machine learning classification, then measurement precision is improved, but loss of time increases
Solution Approach 1:
Sensor data is pre-processed through filtering and feature extraction before being input to the machine learning classifier, preparing the data in advance to accelerate the prediction process and reduce real-time computational delays
Solution Approach 2:
The system processes only the most critical features of sensor data through filtering and passes selected key parameters to the machine learning model, avoiding complete data processing while maintaining sufficient prediction accuracy
Data Source
AI summary
A computer-implemented method for control of a surgical device includes accessing raw data captured by a sensor of the surgical device during a procedure, filtering the raw data with a filter, generating a difference data based on a difference between the raw data and the filtered data, generating zero-crossing data based on determining a point in time where an amplitude of the difference data last crossed from a non-zero amplitude value through a zero amplitude value to a non-zero amplitude value of the opposite sign, providing the zero-crossing data as an input to a machine learning classifier, and predicting a probability of an end stop point based on the machine learning classifier. The end stop point includes a point in time where a knife of the surgical device ceases to cut tissue.