Surgical Robot Network for Error Prevention
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Solution Overview
Problem
Conventional methods for preventing surgical errors and adverse events during surgery are insufficient, leading to unacceptably high adverse event rates despite traditional patient safety initiatives.
Innovation Solution
The development of surgical robot apparatuses with extended operational range, enabled by a surgical robot network that receives medical images, generates 3D renderings, and allows for the execution of robotic movements based on user inputs and stored data files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional communication-based methods are used to prevent surgical errors, then implementation is simple, but effectiveness is insufficient leading to high adverse event rates
Solution Approach 1:
The patent replaces conventional communication-based error prevention methods with an automated robotic surgical system that uses computer vision, machine learning, and real-time image analysis to detect and prevent surgical errors. The system substitutes human communication and judgment with automated technological systems that continuously monitor surgical procedures, analyze medical images, and provide real-time feedback to prevent wrong-site, wrong-person, and wrong-procedure errors.
Solution Approach 2:
The patent introduces an intermediary automated safety system that acts as a mediator between the surgical team and the surgical procedure. This system includes computer vision algorithms, image processing units, and machine learning models that serve as intermediaries to continuously analyze surgical fields, verify surgical sites, and provide real-time safety checks without directly performing the surgical action itself.
2Manufacturing precision
If robotic surgical apparatuses with extended operational range are deployed, then surgical precision and efficiency are enhanced, but device complexity increases
Solution Approach 1:
The patent designs the robotic surgical apparatus to perform multiple functions within a single integrated system. The robotic system can execute various surgical tasks including cutting, suturing, stapling, and real-time imaging analysis. The base module can relocate to different positions, and the robotic arm can perform multiple surgical operations, making the system universal and multi-functional to justify the increased complexity through enhanced precision and versatility.
Solution Approach 2:
The patent employs a nested structure where the robotic arm is mounted on a mobile base module, creating a hierarchical configuration. The base module handles relocation and positioning, while the robotic arm handles precise surgical maneuvers. This nesting allows the system to achieve extended operational range and high precision by dividing functions across nested components, making the complexity manageable through modular architecture.
3Reliability
If real-time monitoring and analysis systems are implemented, then patient safety is improved, but information processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and pre-analyzing medical images before they reach the final decision-making stage. The system uses machine learning models trained on extensive datasets to pre-identify potential risks, anomalies, and critical features in surgical images. This preliminary analysis reduces the information processing load during real-time surgery by pre-filtering and pre-interpreting data, allowing the system to focus computational resources on critical real-time decisions.
Solution Approach 2:
The patent extracts and separates critical safety-related information from the overall data stream for dedicated processing. The system identifies and extracts key features such as surgical site verification, anatomical landmarks, and potential errors, separating them from general surgical imaging data. This extraction allows specialized algorithms to process only the most critical information, reducing overall information processing requirements while maintaining high patient safety monitoring capabilities.
Data Source
AI summary
The present disclosure provides a method for a surgical robot with extended operational range in which a user inputs the surgical robot's movements and locations in a CAD environment through a surgical robot network and the surgical robot movements and locations are stored as a data file. The surgical robot network sends the surgical robot movements and locations data files to the surgical robot which extracts the data file containing the surgical robot movements and locations. The surgical robot then executes the data files containing the surgical robot movements and locations and sends a completion status of the movements and locations and the surgical robot executes a data file containing the robotic movements to perform aspects of the surgical procedure.