Surgical Video Segmentation via Machine Learning Bookmarking
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Solution Overview
Problem
Current surgical video review processes are inefficient, requiring surgeons to manually search and extract relevant segments from lengthy videos, which is time-consuming and cumbersome, especially for training and error analysis.
Innovation Solution
A system utilizing machine learning techniques to automatically bookmark and segment surgical videos based on procedure types and steps, allowing for quick identification and sharing of specific video segments, reducing the need for manual processing and enhancing training and analysis efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If surgeons manually review and search surgical videos, then they can identify relevant segments, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary actions by automatically analyzing and segmenting surgical videos into distinct procedural steps before they are needed for review. Machine learning models pre-process the videos to identify and label different surgical steps, creating an indexed structure that enables rapid retrieval of specific segments without requiring manual search through the entire video.
Solution Approach 2:
The patent divides long surgical videos into smaller, meaningful segments corresponding to different surgical steps or events. This segmentation is achieved through automated detection algorithms that identify temporal boundaries between procedural steps, allowing surgeons to access only the relevant portions of interest rather than reviewing the complete video sequentially.
2Ease of operation
If surgeons manually extract and review specific video segments, then they can focus on relevant content, but the process remains cumbersome and time-consuming
Solution Approach 1:
The system provides self-service functionality where the machine learning models automatically perform the video segmentation and indexing tasks that would otherwise require manual intervention. The system serves itself by autonomously analyzing surgical videos, identifying procedural steps, and creating searchable indexes without requiring surgeon involvement in the preprocessing stages.
Solution Approach 2:
The patent replaces the mechanical process of manual video review and extraction with automated machine learning-based systems. Instead of requiring surgeons to physically search through videos frame-by-frame, the system uses computational algorithms to automatically identify, segment, and retrieve relevant surgical segments based on predefined or learned criteria.
3Loss of information
If surgical videos are reviewed in their entirety, then complete information is available, but the review process becomes inefficient for training and error analysis
Solution Approach 1:
The system extracts and isolates specific surgical steps or events of interest from the complete video record. By identifying and separating relevant segments based on procedural characteristics or annotated metadata, the system enables targeted review of only the necessary portions while maintaining access to the complete video information when needed.
Solution Approach 2:
The patent implements dynamic video review capabilities where the system can adaptively select and present different video segments based on the specific needs of the reviewer. The interface can dynamically adjust which portions of the video are presented first or highlighted, allowing for flexible review strategies that optimize both efficiency and information completeness based on training objectives or error analysis requirements.
Data Source
AI summary
Systems and methods for segmenting surgical videos are disclosed. One example method includes receiving, by a processor of a computing device, surgical video, the surgical video comprising at least a sequence of video frames of a surgical procedure; in response to receiving an identification of a video frame, generating, by the processor, a bookmark based on the video frame; associating, by the processor, the bookmark with the video frame; and storing, by the processor, the bookmark in a non-transitory computer-readable medium.