Medical Video Image Interpretability Characterization
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Solution Overview
Problem
Current video processing technologies fail to effectively characterize the interpretability of images acquired with medical devices, which is crucial for efficient data use and clinical decision-making in medical video analysis.
Innovation Solution
A system and method that characterizes the interpretability of images from medical devices by defining quantitative criteria, storing images in a buffer, and using algorithms to determine and attach outputs to a timeline, allowing users to focus on the most interpretable parts of the video data, with options for displaying and processing these images for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal video segmentation is performed using traditional algorithms (cut-boundary detection, histogram comparisons, motion analysis), then video data can be segmented into temporal segments, but the method fails to effectively characterize the interpretability of images, reducing clinical utility
Solution Approach 1:
The patent introduces new quantitative interpretability criteria (kinematic stability, image quality metrics, information content measures) that transform the traditional segmentation approach. By changing the parameters used for evaluation from simple temporal boundaries to multi-dimensional interpretability metrics, the system achieves both precise interpretability characterization and maintains processing efficiency through automated algorithmic evaluation.
2Loss of information
If all images in a medical video are displayed for review, then complete data is available for clinical decision-making, but the time required to review the data increases significantly
Solution Approach 1:
The patent extracts and highlights only the most interpretable and clinically relevant images from the complete video sequence by applying interpretability criteria. This extraction process identifies key frames that contain the most diagnostic information while filtering out redundant or low-quality images, allowing clinicians to review essential content without the time cost of examining every frame.
Solution Approach 2:
The system performs preliminary automated evaluation of all images against interpretability criteria before clinical review. By pre-characterizing each image's interpretability and organizing them accordingly, the system prepares the data in advance so that clinicians receive pre-filtered, prioritized content rather than raw unprocessed video sequences.
3Quantity of substance
If video data is compressed or summarized to reduce data volume, then storage and transmission efficiency improve, but the quality and interpretability of diagnostic information may be lost
Solution Approach 1:
The patent applies different quality levels and processing approaches to different portions of the video data based on their interpretability characteristics. High-priority interpretable images receive full resolution and detailed analysis, while less critical segments can be compressed or summarized. This local differentiation maintains diagnostic accuracy for important content while reducing overall data volume.
Data Source
AI summary
According to a first aspect, the invention relates to a method to support clinical decision by characterizing images acquired in sequence through a video medical device. The method comprises defining at least one image quantitative criterion, storing sequential images in a buffer, for each image (10) in the buffer, automatically determining, using a first algorithm, at least one output based on said image quantitative criterion and attaching said output to a timeline (11).