Arthroscopic Video Analysis for Real-Time Geometry And Pathology Labeling
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Solution Overview
Problem
Existing arthroscopic surgery lacks real-time analytical capabilities for identifying anatomical structures, pathologies, and measuring geometry due to the complexity of processing arthroscopic video data, limiting contextual understanding and hindering the application of machine learning and artificial intelligence in this field.
Innovation Solution
Implementing methods and systems for real-time arthroscopic video analysis using computer-assisted surgical systems, including electromagnetic sensor devices and machine learning models to label anatomical structures, identify pathologies, and measure geometry during surgeries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine learning and artificial intelligence are applied to analyze arthroscopic video data, then contextual understanding of anatomical structures and pathologies is improved, but processing complexity and computational requirements increase significantly
Solution Approach 1:
The video analysis system segments the arthroscopic video into individual frames and extracts key features from each frame independently. This segmentation allows the complex video data to be processed in manageable units, reducing the computational burden while maintaining comprehensive contextual understanding of anatomical structures and surgical procedures.
Solution Approach 2:
The system performs preliminary processing by pre-training machine learning models on extensive datasets of arthroscopic videos and anatomical structures. This preliminary action prepares the models to quickly recognize and identify anatomical structures, pathologies, and surgical techniques during actual surgery, reducing real-time processing complexity while enhancing contextual understanding.
2Measurement precision
If real-time video analysis is implemented during arthroscopic surgery, then surgical accuracy and decision-making are improved, but computational resources and processing time requirements increase
Solution Approach 1:
The analysis system applies local quality by focusing computational resources on specific regions of interest within the arthroscopic video frames. Instead of processing the entire video stream uniformly, the system identifies and analyzes only the most critical anatomical structures and potential pathologies, reducing overall computational resource consumption while maintaining high surgical accuracy where needed.
Solution Approach 2:
The system implements partial action by providing real-time analysis of the most critical surgical parameters and anatomical structures, while summarizing or reducing detailed analysis of less critical areas. This approach delivers sufficient surgical accuracy for decision-making without the excessive computational resources required for complete exhaustive analysis of all video data.
3Loss of information
If comprehensive video data is recorded and analyzed, then surgical information and analytics are improved, but data storage requirements and data management complexity increase
Solution Approach 1:
The system extracts only the essential surgical information and key features from the comprehensive arthroscopic video data. By extracting and storing only the most relevant anatomical structure identifications, pathology detections, and surgical technique annotations, the system reduces data storage requirements while preserving the critical surgical information needed for analysis and reference.
Solution Approach 2:
The system creates compressed representations and summaries of the surgical video data, storing condensed information about anatomical structures, procedures, and outcomes rather than the complete raw video streams. This copying approach maintains surgical information integrity while significantly reducing data storage requirements and management complexity.
Data Source
AI summary
Methods, non-transitory computer readable media, and arthroscopic video analysis apparatuses and systems that facilitate improved analysis of videos of arthroscopic procedures are disclosed. With this technology, analytical data related to the video feed of an arthroscopic surgery can be obtained using machine learning models and associated with the video feed. The generated videos can be output in real-time to provide contextual information related to the surgical procedure, or can be saved for playback for training or informational purposes.