Computer-Vision Surgical Tool Control for In-View Safety Gating
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Solution Overview
Problem
Existing surgical tools can cause injuries due to erroneous handling during procedures, necessitating improved safety and reliability in their control during surgeries.
Innovation Solution
The implementation of computer-vision processing systems that train machine-learning models to recognize surgical tools and anatomical structures from images, enabling the control or facilitation of surgical tools based on their presence and position within the camera's field of view, thereby ensuring safe and reliable operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If surgical tools are made more powerful and capable to improve surgical effectiveness, then surgical productivity and effectiveness improve, but the risk of injury to the patient increases due to erroneous handling
Solution Approach 1:
The system continuously captures video feeds from cameras during surgery, processes them through machine learning models to detect surgical tools and anatomical structures, and provides real-time feedback by enabling or disabling tool functions based on detected positions. This closed-loop feedback mechanism ensures that powerful surgical tools only operate when properly positioned, reducing injury risk while maintaining surgical effectiveness.
Solution Approach 2:
The computer vision system acts as an intermediary between the surgeon and the surgical tools. It processes visual information and translates it into control signals that enable or disable tool functions, serving as a safety mediator that prevents erroneous handling while allowing effective surgery when conditions are appropriate.
2Reliability
If computer-vision systems are implemented to improve safety by controlling surgical tools, then patient safety improves, but device complexity increases
Solution Approach 1:
The system uses a single computer vision platform that can detect multiple types of surgical tools (scissors, forceps, energy devices, etc.) and anatomical structures through unified machine learning models. This multi-functional approach improves safety across diverse surgical scenarios while avoiding the need for separate specialized systems for each tool type, thereby limiting the increase in overall complexity.
Solution Approach 2:
The machine learning models automatically detect and track surgical tools and anatomical structures without requiring manual input or configuration during surgery. The system self-adjusts by enabling or disabling tool functions based on autonomous detection, reducing the operational complexity for surgeons while maintaining high safety standards.
3Measurement precision
If machine-learning models are trained to recognize surgical tools and anatomical structures in real-time, then measurement precision of tool position improves, but loss of time for training and processing increases
Solution Approach 1:
Machine learning models are trained offline before surgical procedures using extensive datasets of surgical images and videos. This preliminary training ensures that when the models are deployed during surgery, they can rapidly and accurately detect tools and anatomical structures in real-time video feeds without requiring training time during the actual procedure, thus achieving high measurement precision without sacrificing surgical time.
Data Source
AI summary
The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.