Surgical Video Annotation via Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual annotation of surgical videos is time-consuming, repetitive, and prone to errors, leading to undesirable information loss, especially in long or routine procedures.
Innovation Solution
An apparatus and method that use a processor and machine learning algorithm to automatically annotate surgical videos based on commands, instrument settings, and video parameters, reducing user intervention and increasing annotation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual annotation is used, then user control over annotation content is maintained, but time consumption increases and errors occur
Solution Approach 1:
The system performs self-annotation by automatically detecting surgical instruments, anatomical structures, and procedure steps from video feeds without requiring manual user input. The annotation system processes video data independently to generate timestamps and descriptive annotations, eliminating the need for users to manually review and mark each annotation point.
Solution Approach 2:
Manual mechanical annotation processes are replaced with automated computational systems that use image processing, object detection algorithms, and temporal analysis to automatically generate annotations. The system substitutes human manual operations with automated software that processes video frames to identify and annotate surgical events.
2Productivity
If automation is implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The annotation system is divided into separate functional modules: video processing module, object detection module, anatomical structure identification module, and annotation generation module. Each module handles a specific aspect of the annotation task independently, making the overall complex system manageable through modular architecture where each component can be optimized separately.
Solution Approach 2:
The system uses a multi-functional approach where a single integrated platform performs multiple tasks including video processing, object detection, anatomical structure recognition, instrument identification, and annotation generation. This universal system handles diverse annotation requirements through unified algorithms and data processing pipelines.
3Loss of information
If more annotation details are added, then pedagogical value increases, but information overload occurs
Solution Approach 1:
The system applies different levels of detail to different annotation types based on their importance and relevance. Critical surgical events receive detailed annotations with multiple parameters, while routine procedures receive summarized annotations. The system dynamically adjusts annotation granularity based on the surgical context and identified event significance, preventing information overload in routine areas while maintaining detail where needed.
Solution Approach 2:
The system generates comprehensive annotations that may include more detail than immediately apparent, but structures the information hierarchically with primary annotations containing essential information and secondary annotations providing additional context. This partial action approach ensures complete information capture while organizing it in a way that prevents overwhelming the user.
Data Source
AI summary
An apparatus configured to annotate a video of a surgical procedure is disclosed. The annotation can be based on at least one of a command executed during the surgical procedure. an instrument setting, or the video.