Computer-Vision Surgical Tool Gating by Field-of-View Detection
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Solution Overview
Problem
Surgical tools can cause injuries due to erroneous handling during procedures, necessitating improved safety and reliability measures.
Innovation Solution
The implementation of computer-vision processing systems that train machine-learning models to recognize surgical tools and anatomical structures from images, enabling controlled operation of surgical tools based on their presence and position within the camera's field of view, ensuring safe and reliable functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgical tools are used during surgical procedures, then surgical operations can be performed, but patient safety is compromised due to erroneous handling
Solution Approach 1:
The system continuously monitors the surgical field using a camera and machine learning model to detect the surgical tool's position and context. Based on this feedback, the system dynamically enables or disables tool functionality through software control, creating a closed-loop safety mechanism that responds to real-time surgical conditions
Solution Approach 2:
A computer vision processing system acts as an intermediary between the surgeon and the surgical tool. The system processes video feeds, recognizes surgical contexts using machine learning, and controls tool activation through software intermediaries, adding a layer of intelligent mediation that prevents erroneous tool activation
2Reliability
If computer-vision processing systems are implemented to control surgical tools, then patient safety is improved, but device complexity increases
Solution Approach 1:
The system uses a multi-functional integrated approach where a single computer vision processing system performs multiple tasks: detecting surgical tools, recognizing anatomical structures, determining surgical context, and controlling tool activation. This consolidates what could be multiple separate systems into one unified platform
Solution Approach 2:
The machine learning model automatically trains and improves itself by processing surgical video data, enabling the system to self-enhance its recognition and control capabilities without requiring manual reconfiguration. The system autonomously adapts to different surgical scenarios and tool types
3Measurement precision
If machine-learning models are trained to recognize surgical tools from images, then tool detection accuracy is improved, but loss of time occurs during training and processing
Solution Approach 1:
The machine learning model is trained in advance on comprehensive datasets of surgical tools and anatomical structures before actual surgical use. This preliminary training phase, though time-consuming, is performed offline so that the model is ready for rapid real-time inference during surgery without causing delays
Solution Approach 2:
Once trained, the model continuously processes video feeds in real-time during surgical procedures, maintaining constant surveillance of the surgical field. The system operates continuously without interruption, enabling immediate detection and control responses throughout the entire surgical procedure
Data Source
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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.