Anatomy-Aware Surgical Annotations That Adapt to Camera Movement
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Solution Overview
Problem
Existing surgical annotation systems lack the ability to efficiently create and maintain annotations on anatomical structures during surgical procedures, particularly when the camera view changes, and do not effectively utilize computer vision for automatic identification and tracking.
Innovation Solution
A system that combines user input devices with computer vision to create and track surgical annotations, allowing for real-time digital overlays on anatomical structures, which can be automatically detected and updated as the camera view changes, and integrates with robotic surgical systems for enhanced precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation methods are used by surgeons, then annotations can be created for training and documentation purposes, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system enables automatic annotation by allowing the surgical site images themselves to serve as the input for the annotation process. Computer vision algorithms automatically identify and annotate anatomical structures, surgical instruments, and procedural steps without requiring manual intervention from surgeons, thereby dramatically reducing the time and effort required for annotation creation.
Solution Approach 2:
The patent replaces the manual mechanical process of annotation (surgeons manually drawing or labeling on images) with an automated computer vision system that uses image processing algorithms to automatically detect, track, and annotate surgical elements. This substitution of mechanical human effort with automated computational processes significantly improves annotation productivity.
2Reliability
If annotations are created manually, then they can be added to surgical videos, but they do not automatically update when the camera view changes
Solution Approach 1:
The system implements continuous feedback loops where the computer vision algorithm constantly monitors the surgical video feed, detects anatomical structures and instruments in real-time, and automatically updates annotations to reflect current camera views. This closed-loop feedback mechanism ensures annotations remain accurate and properly positioned even as the camera moves or changes angle during surgery.
Solution Approach 2:
The annotation system transitions from static manual annotations to dynamic automated annotations that continuously adapt to changing camera views. The computer vision algorithms track the movement of surgical elements and automatically adjust annotation positions, orientations, and content in real-time, making the annotations flexible and adaptive rather than fixed and static.
3Productivity
If computer vision is used for automatic identification, then annotation efficiency improves, but system complexity increases
Solution Approach 1:
The system integrates multiple functions into a single computer vision platform that can perform various tasks including anatomical structure identification, surgical instrument detection, procedural step recognition, and automatic annotation generation. This multi-functional approach consolidates what would otherwise require separate systems into one unified solution, managing complexity while maintaining high productivity.
Data Source
AI summary
A system and method allow creation of surgical annotations during surgery. A digital image of a treatment site in a region of patient anatomy is captured using a camera and is displayed on an image display. The system displays an overlay marking an anatomic region of interest in the displayed image. During the course of the surgery, panning of the image results in the anatomic region of interest being outside the displayed field of view. In response to user input, the displayed image is subsequently panned such that the anatomic region of interest and the displayed overlay return to the displayed field of view.