Surgical Video Phase Clustering for Objective Workflow Variation Analysis
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Solution Overview
Problem
The analysis of large volumes of surgical video data to identify commonalities in surgical procedures is highly subjective and error-prone due to varying factors such as patient condition and physician preferences, lacking objective and standardized methods to quantify surgical performance.
Innovation Solution
A machine learning-based system segments surgical videos into phases, clusters similar workflows, and identifies unique surgical approaches using distance metrics and clustering techniques, providing graphical representations for display and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of surgical videos is performed to identify commonalities and variations, then detailed examination of surgical workflows is possible, but the process becomes highly subjective and error-prone
Solution Approach 1:
The patent replaces manual mechanical analysis of surgical videos with an automated computer-based system that uses machine learning models and algorithms to objectively identify and compare surgical approaches, eliminating subjectivity and human error while maintaining high precision in workflow analysis
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between raw surgical video data and human interpretation, using structured data extraction, phase identification, and comparison algorithms to bridge the gap between complex video content and meaningful surgical insights
2Reliability
If automated systems are used to analyze surgical videos, then objectivity and consistency improve, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex task of surgical video analysis into distinct phases including video preprocessing, phase identification, workflow extraction, and comparison. This segmentation allows each component to be optimized independently, improving reliability while managing overall system complexity through modular architecture
Solution Approach 2:
The patent transforms surgical video data into standardized parameters and metrics that can be objectively compared across different procedures. By changing the representation of surgical workflows into quantifiable phases and sequences, the system achieves high consistency and reliability in identifying surgical approaches
3Loss of information
If detailed analysis of multiple surgical videos is performed to identify variations, then comprehensive understanding of surgical approaches is achieved, but time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing surgical videos to identify and mark key phases and transitions before detailed analysis. This preliminary structuring of video data enables faster subsequent processing and comparison, reducing overall analysis time while preserving complete workflow information
Solution Approach 2:
The patent extracts only the essential surgical workflow information and key phases from comprehensive video data, separating critical information from redundant content. This extraction process maintains completeness of meaningful surgical details while significantly reducing the data volume requiring detailed analysis
Data Source
AI summary
An aspect includes a computer-implemented method that identifies variations in surgical approaches to medical procedures. Surgical videos documenting multiple cases of a medical procedure are analyzed to identify different surgical approaches used by service providers when performing the medical procedure. According to some aspects surgical phases are identified in each surgical video and groups of similar surgical phase sequences are grouped into surgical approaches.