Robotic Surgical Control Using Machine-Learned Operation Commands
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Solution Overview
Problem
Conventional surgical robots rely heavily on the operator's skill and capability, leading to inefficiencies in surgical operations.
Innovation Solution
A surgical system with an instrument manipulator, control device, and user interface that includes an operation processing module to generate automatic operational commands, enabling automated surgical procedures and incorporating machine learning models to improve accuracy and adapt to the operator's skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a robot performs surgical operations based on operator input, then the surgical operation can be performed with robotic assistance, but the operation result greatly depends on the operator's surgical capability
Solution Approach 1:
The robot is equipped with machine learning models that enable it to learn and improve surgical operations autonomously. The system performs self-service by automatically acquiring surgical data, training its own models, and enhancing its operational capabilities without continuous human intervention, thereby reducing dependence on individual operator skills while maintaining high reliability
Solution Approach 2:
The system implements feedback mechanisms where surgical operation data is collected, processed, and used to train machine learning models. This feedback loop allows the robot to continuously improve its performance based on actual surgical outcomes, ensuring consistent and reliable results that are not dependent on any single operator's capability
2Ease of operation
If manual operation is used, then the operator can control the robot, but surgical efficiency is reduced due to dependence on operator capability
Solution Approach 1:
The system dynamically adjusts the level of automation based on the surgical situation. The robot can switch between fully manual operation (when operator control is needed) and fully automatic operation (when efficiency is prioritized), with machine learning models progressively taking over more tasks to enhance surgical efficiency while preserving operator control when necessary
Solution Approach 2:
The machine learning models are trained in advance using accumulated surgical data, enabling the robot to perform complex surgical tasks automatically without real-time operator intervention. This preliminary training action allows the system to execute pre-planned surgical sequences with high efficiency, reducing the burden on operators and accelerating surgical procedures
Data Source
AI summary
A surgical system includes a robot including an instrument manipulator that has a surgical instrument, a control device that controls the robot, and a user interface that receives an input of a command and outputs the command to the control device. The control device generates an automatic operational command for causing the robot to automatically perform a surgical operation, and controls the surgical operation of the robot according to the automatic operational command.