Microsurgical Intervention Classification via Metadata and Video Analysis
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Solution Overview
Problem
Existing methods for determining the type of microsurgical intervention based on machine-learning techniques are inaccurate and struggle to handle a wide spectrum of microsurgical interventions across multiple specialties.
Innovation Solution
A multi-stage classification approach that first assesses metadata associated with the microsurgical video to determine if the type of intervention can be unambiguously identified, and if not, performs an analysis of the video data to make an accurate determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine-learning techniques are used to determine the type of microsurgical intervention, then automation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the classification task into multiple stages: first analyzing metadata (procedure codes, surgeon information, facility data) to generate candidate intervention types, then analyzing video data to confirm or refine the classification. This multi-stage segmentation allows each stage to specialize in specific features, improving overall accuracy while maintaining automation.
Solution Approach 2:
The patent introduces metadata analysis as an intermediary step between raw video input and final classification output. The metadata provides contextual information that guides the video analysis, acting as a mediator that improves classification accuracy without requiring full manual review of video content.
2Adaptability or versatility
If machine-learning techniques are trained to handle a wide spectrum of microsurgical interventions, then adaptability is improved, but reliability deteriorates
Solution Approach 1:
The patent segments the diverse set of microsurgical interventions into organized hierarchies (e.g., ophthalmology, neurosurgery, ENT) with structured taxonomies. This segmentation allows the system to adapt to new specialties by adding to the hierarchy rather than retraining the entire system, while maintaining reliable classification within each specialized category.
Solution Approach 2:
The patent implements a dynamic classification system where the analysis depth and methodology adapt based on the confidence level of initial metadata-based classification. For clear-cut cases, the system provides rapid automated classification; for ambiguous cases, it engages more sophisticated video analysis, creating a dynamic response that maintains both versatility and reliability.
3Productivity
If metadata alone is used to determine the type of microsurgical intervention, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent performs preliminary analysis of metadata (procedure codes, surgeon credentials, facility information) before conducting video analysis. This preliminary action quickly eliminates impossible intervention types and narrows down candidate classifications, enabling rapid processing for cases where metadata provides sufficient information while preparing the groundwork for more detailed video analysis when needed.
Solution Approach 2:
The patent creates a dynamic workflow that adapts the level of analysis based on metadata quality and case complexity. For routine cases with clear metadata indicators, the system achieves rapid classification using only metadata. For complex or ambiguous cases, the system dynamically transitions to incorporate video analysis, balancing productivity and precision based on actual case requirements.
Data Source
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AI summary
Various types of the disclosure pertain to determining a type of a microsurgical intervention. The type of the microsurgical intervention is determined based on metadata associated with video data encoding a microsurgical video. It is optionally possible to take into account the video data when determining the type of the microsurgical intervention.