Graphical Tagging for Robotic Surgery Visual Recall
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Solution Overview
Problem
Current surgical robotic systems lack the ability to dynamically tag or bookmark anatomical structures within the surgical space, making it difficult for surgeons to navigate under compromised visual conditions such as blood or smoke obstructions.
Innovation Solution
A system that uses 2D, 3D, and structured light imaging sources to create a visual model of the surgical site, allowing surgeons to tag and bookmark anatomical structures. These tags are then displayed as graphical overlays on the endoscopic view, enabling the surgeon to recall and monitor their positions visually, even under obscured conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surgeons rely on direct endoscopic visualization during robotic surgery, then they can observe anatomical structures in real-time, but visual conditions become compromised due to blood, smoke, or other obstructions making it difficult to locate critical structures
Solution Approach 1:
The system performs preliminary actions by automatically detecting and tagging anatomical structures in the surgical field before visual obstructions occur. The robotic system uses computer vision and image processing to identify structures such as blood vessels, nerves, and organs, then places graphical tags on them. These tags serve as persistent visual markers that remain visible even when the actual anatomical structures become obscured by blood, smoke, or tissue manipulation, thereby preserving location information throughout the procedure.
Solution Approach 2:
The system creates a visual copy or representation of the surgical field by generating a digital model that includes tagged anatomical structures. Instead of relying solely on direct visualization of the physical surgical field, the system produces a graphical overlay containing copies of structure locations and identities. This digital copy persists through visual obstructions and can be recalled and monitored throughout the procedure, allowing surgeons to maintain awareness of critical structures even when direct view is compromised.
2Manufacturing precision
If the robotic system provides detailed visual information about anatomical structures, then surgical precision improves, but the system complexity increases due to the need for structure detection, tagging, and recall mechanisms
Solution Approach 1:
The robotic surgical system performs self-service by automatically detecting, identifying, and tagging anatomical structures without requiring manual intervention from the surgeon. The system uses integrated computer vision algorithms and image processing capabilities to autonomously analyze endoscopic images, identify anatomical features, and place appropriate tags. This automation reduces the complexity burden on the surgeon while maintaining high surgical precision through consistent and accurate structure identification throughout the procedure.
Solution Approach 2:
The robotic system incorporates multi-functionality by integrating multiple capabilities into a single unified platform: real-time image processing, anatomical structure detection, automatic tagging, tag management, and visual recall. Rather than requiring separate systems for each function, the robotic surgical system performs all these tasks through integrated software modules and algorithms, thereby managing complexity through consolidation while providing comprehensive visual assistance for enhanced surgical precision.
3Ease of operation
If surgeons manually track and remember anatomical structure positions during surgery, then they can navigate the surgical space, but this becomes difficult under compromised visual conditions caused by blood or smoke
Solution Approach 1:
The system implements continuous feedback by maintaining and displaying updated information about tagged anatomical structures throughout the surgical procedure. When the robotic system detects changes in the surgical field or when the surgeon requests recall, the system provides feedback by displaying the current positions and statuses of tagged structures through graphical overlays. This persistent visual feedback enables surgeons to navigate the surgical space reliably even when direct visualization is compromised, as the tag positions are maintained and can be recalled at any time.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and tagging all relevant anatomical structures before visual obstructions occur. These pre-placed tags serve as persistent reference points that remain valid throughout the procedure. When visual conditions deteriorate due to blood, smoke, or tissue manipulation, surgeons can rely on these pre-established tags for navigation without needing to manually re-track or re-remember structure positions, thereby maintaining navigation ease and reliability throughout the procedure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enhances surgical precision and safety by allowing surgeons to navigate the surgical space more effectively under compromised visual conditions, reducing the risk of instrument contact with critical anatomical structures and improving overall procedural control.
Implementation Method 1
One type of surface mapping method is one using structured light. Structured light techniques are used in a variety of contexts to generate three-dimensional (3D) maps or models of surfaces. These techniques include projecting a pattern of structured light (e.g. a grid or a series of stripes) onto an object or surface. One or more cameras capture an image of the projected pattern. From the captured images the system can determine the distance between the camera and the surface at various points, allowing the topography/shape of the surface to be determined.
Data Source
AI summary
A system and method for augmenting an endoscopic display during a medical procedure including capturing a real-time image of a working space within a body cavity during a medical procedure. A feature of interest in the image is identified to the system using eye tracking input, and a graphical tag is displayed on the image marking the feature.