Arthroscopic Video Analysis for Real-Time Anatomy and Pathology Detection
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Solution Overview
Problem
Existing arthroscopic surgery lacks real-time analytics 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 in the arthroscopic field of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning and artificial intelligence are applied to arthroscopic video analysis, then real-time analytics for identifying anatomical structures and pathologies can be provided, but the processing complexity and computational requirements increase significantly
Solution Approach 1:
The video analysis system is divided into separate functional modules: a first processor that performs initial video processing and feature extraction, and a second processor that executes machine learning models for anatomical structure identification and pathology detection. This segmentation allows each component to be optimized independently and reduces the computational burden on any single system.
Solution Approach 2:
The patent introduces an intermediary processing layer that bridges raw arthroscopic video data and the machine learning analysis. This intermediate processing stage pre-processes the video data, extracts relevant features, and prepares it for consumption by the machine learning models, thereby reducing the complexity of direct video-to-insight processing.
2Loss of time
If real-time video analysis is implemented during arthroscopic surgery, then contextual understanding can be provided at the refresh rate of the monitor, but the computational resources and processing time requirements increase
Solution Approach 1:
The system performs preliminary processing of the arthroscopic video stream in advance, extracting key frames and anatomical features before the actual analysis is needed. This pre-processing allows the machine learning models to operate on pre-prepared data, reducing real-time computational requirements and enabling faster response times during surgery.
Solution Approach 2:
Instead of processing every single frame of video data in real-time, the system selectively processes only the most critical frames and regions. This partial action approach maintains acceptable response times while significantly reducing the computational resources required compared to full-frame real-time processing.
3Loss of information
If arthroscopic video is recorded and analyzed, then anatomical structures and pathologies can be identified, but the volume of data to be processed increases
Solution Approach 1:
The system extracts and focuses on the most relevant information from the arthroscopic video stream, such as key anatomical structures and pathological features. By selectively extracting only the necessary data rather than processing the entire video stream, the system maintains information completeness while reducing the quantity of data that requires analysis.
Solution Approach 2:
The analysis system applies different processing quality levels to different regions of the video. High-resolution analysis is applied only to regions containing anatomical structures of interest, while lower-resolution processing is applied to background areas. This local quality approach preserves critical information while reducing overall data processing volume.
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.