Surgical Video Interface for Selective Structure Identification
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Solution Overview
Problem
Existing surgical systems face challenges in effectively distinguishing and conveying information about detected structures, particularly anatomical and surgical instruments, due to dynamic and unpredictable environments, leading to potential misidentification and complications.
Innovation Solution
A computer-assisted system using machine learning models to detect and identify structures in real-time surgical data, providing a user interface with dynamic graphical overlays and symbols that highlight structures only when they are in the field of view, enhancing visibility and reducing clutter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graphical overlays and symbols are displayed to indicate detected structures, then visibility and identification of structures is improved, but visual clutter and obstruction of the surgical field increases
Solution Approach 1:
The patent applies local quality by displaying visual indicators selectively - only for detected structures that are relevant to the current surgical context and located within the field of view. Different visual attributes (colors, shapes, positions) are assigned to different structure types, providing localized information enhancement without uniformly cluttering the entire display.
Solution Approach 2:
The patent segments the visual information by separating structure indicators from the surgical video feed using distinct graphical overlays and a separate toolbar interface. This segmentation allows surgeons to perceive structural information without it being visually integrated with and potentially confusing the surgical field view.
2Reliability
If machine learning models process surgical data in real-time to detect structures, then surgical precision and safety are improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent implements a universal machine learning model architecture that can detect multiple different structure types (anatomical structures, surgical instruments, artifacts) using the same computational framework. This multi-functionality reduces overall system complexity compared to having separate detection systems for each structure type.
Solution Approach 2:
The patent introduces an intermediary processing layer that translates complex machine learning detection outputs into simplified visual indicators and structured data. This intermediary layer manages the complexity by abstracting the sophisticated computational processes into manageable visual representations for the surgeon.
3Loss of information
If visual indicators are displayed for all detected structures, then complete information is provided, but attention distraction and cognitive load increase
Solution Approach 1:
The patent uses color changes and visual attribute modifications to encode different structure types and states (e.g., different colors for anatomical structures versus surgical instruments, different states for detected versus confirmed structures). This visual encoding allows comprehensive information presentation while maintaining ease of interpretation through intuitive color-coding schemes.
Data Source
AI summary
Technical solutions are provided to facilitate computer assistance during a surgery to prevent complications by detecting. identifying. and indicating the identification of certain structures in a field of view of a video of the surgery. According to some aspects. a computer vision system is trained to detect several structures in the video of the surgery, and further to distinguish between the structures. Further, a user interface element is displayed that indicates the identification of the structure by updating a visual attribute of the user interface clement to match a visual attribute used to represent the indicated structure itself.