Automated Surgical Video Analysis for Event Detection and Plane Deviation
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Solution Overview
Problem
Current systems lack efficient methods for automatically analyzing surgical videos to support medical professionals during procedures, review performance statistics, and assign surgical teams effectively, especially in identifying surgical planes and assessing competency.
Innovation Solution
The development of systems and methods that analyze video frames to identify surgical events, detect deviations from surgical planes, and assess competency through machine learning algorithms, enabling the aggregation and presentation of statistical data linked to video evidence and surgical team assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If surgical videos are manually reviewed to identify surgical events and planes, then accuracy in identifying surgical events is improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical review of surgical videos with automated image processing and machine learning algorithms. The system automatically analyzes video frames to identify surgical events, instruments, and planes, substituting human visual inspection with computational analysis that achieves both high accuracy and rapid processing.
Solution Approach 2:
The surgical video analysis system performs self-service by automatically identifying and categorizing surgical events without requiring manual intervention. The machine learning model autonomously processes video data, detects surgical planes, and generates annotations, enabling the system to serve its own analytical needs without external human input.
2Adaptability or versatility
If comprehensive statistical data is aggregated from multiple surgical videos, then performance analysis capability is improved, but data processing complexity and computational resources deteriorate
Solution Approach 1:
The patent segments the complex task of comprehensive video analysis into distinct modular components: video frame processing, surgical event detection, statistical data extraction, and performance metric calculation. Each module handles a specific aspect of analysis independently, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The surgical video analysis system is designed as a universal platform that can analyze multiple types of surgical videos, extract various statistical data, and generate different performance metrics from a single integrated system. The machine learning model is trained to handle diverse surgical procedures and events, providing multi-functional analysis capability.
3Reliability
If real-time video analysis is performed during ongoing surgical procedures, then intraoperative support quality is improved, but computational load and processing time deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive surgical video datasets before deployment. During actual surgical procedures, the pre-trained models require minimal computational resources to analyze video streams in real-time, as the heavy computational burden of model training has already been completed beforehand.
Solution Approach 2:
The patent implements partial analysis by focusing computational resources on detecting only the most critical surgical events and planes during ongoing procedures. Rather than analyzing every aspect of the video in real-time, the system prioritizes detection of key surgical milestones and potential safety concerns, reducing computational load while maintaining reliable intraoperative support.
Data Source
AI summary
Systems, methods, and computer readable media related to aggregating and associated captured medical data are disclosed. They involve receiving an ID of a piece of equipment in a medical facility, location information for the equipment, and medical information captured by the equipment; and ascertaining a time of information capture by the equipment. They further involve performing a lookup in a data record to determine an identity of a particular patient assigned to a location associated with the location information and performing a lookup in a data structure to identify a medical record of the particular patient. They further involve establishing an association between the medical information captured by the equipment and the medical record to thereby enable access to the medical information through access to the medical record of the particular patient.