Surgical Video Condensation via Tool-Based Stage Identification
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Solution Overview
Problem
Video recordings of surgical procedures are often lengthy and contain non-informative footage, making them inefficient for educational or documentation purposes due to their large size and low clinical value.
Innovation Solution
A method involving a computational model trained on labeled images of surgical procedures to associate runtime images with stages of the procedure based on characteristics of surgical tools, using convolutional neural networks (CNN) and hidden Markov models (HMM), which generates a condensed version of the video by discarding non-informative frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video recordings of surgical procedures are kept complete and unedited, then all procedural information is preserved, but the video length and storage space requirements increase significantly
Solution Approach 1:
The system extracts and removes non-informative frames from surgical videos based on computational model analysis. The model identifies frames containing mundane tasks, blurry images, or external areas and selectively removes them while preserving clinically valuable frames, thus reducing video length without losing essential procedural information.
Solution Approach 2:
The system discards non-essential video frames that do not contribute to clinical value and recovers only the informative portions. This selective discarding process reduces overall video duration while maintaining the essential surgical procedure information needed for educational and documentation purposes.
2Reliability
If all frames from surgical videos are retained, then complete documentation is achieved, but storage space requirements become excessive
Solution Approach 1:
The computational model extracts and identifies non-informative frames through image analysis, separating them from clinically valuable frames. This extraction process enables selective retention of only necessary frames for documentation, significantly reducing storage requirements while maintaining documentation reliability.
Solution Approach 2:
The system changes the parameter of frame selection from retaining all frames to retaining only frames that meet specific criteria (clinical value, clarity, relevance). This parameter change in frame selection strategy reduces storage space requirements while preserving documentation completeness.
3Adaptability or versatility
If complete surgical videos are used for training, then comprehensive procedural coverage is provided, but the time required to review and learn from videos increases
Solution Approach 1:
The system extracts and removes frames depicting mundane tasks and non-essential activities from surgical videos. This extraction creates a condensed version that covers all essential procedural steps while eliminating time-consuming portions, reducing review time without compromising educational coverage.
Solution Approach 2:
The system discards frames that do not contribute to educational value and recovers only the essential procedural information. This selective process maintains comprehensive procedural coverage for training purposes while significantly reducing the time required to review and learn from the videos.
4Extent of automation
If computational models are trained to analyze surgical images, then automated stage identification is achieved, but system complexity increases
Solution Approach 1:
The computational model is designed to perform multiple functions: identifying surgical stages, detecting surgical tools, analyzing image characteristics, and determining frame informality. This multi-functionality achieves automated stage identification while consolidating complexity into a single versatile system rather than multiple separate systems.
Data Source
AI summary
A method includes accessing training images that collectively depict multiple stages of a surgical procedure. The method also includes accessing labels that indicate, for each of the training images, characteristics of one or more surgical tools depicted and a stage of the multiple stages of the surgical procedure depicted. The method also includes training a computational model, using the training images and the labels, to associate runtime images with a stage of the multiple stages based on characteristics of one or more surgical tools that are depicted by the runtime images. Another method includes associating, using a computational model, runtime images with a stage of a surgical procedure based on characteristics of one or more surgical tools depicted by the runtime images and generating output that indicates the stage associated with each of the runtime images.