Arthroscopic Video Segmentation Using Machine Learning
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Solution Overview
Problem
Current arthroscopic surgery video analysis systems require manual segmentation by surgeons, which is time-consuming and distracting during procedures, and lack efficient navigation tools for reviewing or sharing specific video segments post-surgery.
Innovation Solution
An automated video segmentation system using machine learning models processes arthroscopic video data in real-time to identify segments and generate tags, allowing for easy navigation and annotation of surgical procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation is used by surgeons, then video can be segmented according to surgical activities, but surgeon's focus is distracted and time is consumed
Solution Approach 1:
The system performs automatic video segmentation using machine learning models that analyze surgical video data and identify key events, surgical steps, and anatomical structures without requiring surgeon intervention. The AI processing unit independently segments the video and generates tags, allowing the system to serve itself rather than requiring manual surgeon input for each segmentation task.
Solution Approach 2:
The patent replaces the manual mechanical process of surgeon-operated segmentation with an automated computational system. Machine learning models process video frames, detect surgical events, and generate segmentation tags automatically, substituting the surgeon's manual clicking and tagging actions with algorithmic video analysis and automatic event detection.
2Reliability
If continuous video recording is used, then complete surgical procedure is captured, but navigation and review of specific segments is difficult
Solution Approach 1:
The system divides the continuous surgical video into distinct segments based on detected surgical events, anatomical structures, and procedural steps. Each segment is tagged with metadata identifying its content, allowing surgeons to quickly navigate to specific portions of the surgery by searching for relevant tags rather than scrubbing through the entire continuous video recording.
Solution Approach 2:
The patent introduces an intermediary indexing system that bridges the complete video record and efficient navigation. Machine learning-generated tags and segment markers serve as intermediaries, allowing surgeons to search and jump to specific surgical events without manually reviewing the entire continuous video, thus maintaining completeness while enabling efficient access.
3Ease of operation
If video snippets are created manually, then specific segments are isolated, but context information is lost and review is time-consuming
Solution Approach 1:
The system creates video segments that serve multiple functions simultaneously: they isolate specific surgical events for focused review while preserving contextual information through hierarchical tagging. Each segment is tagged with multiple metadata labels indicating surgical step, anatomical structure, instruments used, and temporal relationships to other events, allowing a single segment to provide both isolation and context.
Solution Approach 2:
The patent implements a nested segmentation structure where video clips are nested within broader surgical phases, which are nested within the complete surgical procedure. Each level of nesting preserves context from parent levels while allowing detailed review at the clip level. Tags at multiple hierarchical levels maintain contextual relationships without requiring loss of information.
Data Source
AI summary
Methods, non-transitory computer readable media, and arthroscopic video segmentation apparatuses and systems that facilitate improved, automatic segmentation analysis of videos of arthroscopic procedures are disclosed. With this technology, a video feed of an arthroscopic surgery can be automatically segmented using machine learning models and one or more tags related to the segments can be associated with the video feed. The generated videos can be output in real time to provide segmented information related to the surgical procedure or can be saved with the one or more segments tagged for playback for training or informational purposes.