Medical Imaging UI Control for Target Frame Retrieval
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Solution Overview
Problem
Existing medical imaging systems often fail to utilize a significant portion of previously acquired image data, leading to the possibility of missing abnormalities or pathologies that were not recognized during earlier procedures, hindering effective assessment and diagnosis.
Innovation Solution
A computer-implemented method for identifying target frames in stored medical video data that contain representations of target elements, such as anatomical or annotation features, and controlling the user interface to provide relevant information, using techniques like character or object detection algorithms to efficiently process and display these frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If only a small portion of previously acquired image data is made available to the operator, then the system is easier to operate and less complex, but the reliability of diagnosis is reduced due to missing abnormalities or pathologies
Solution Approach 1:
The system performs preliminary computer-implemented identification of target frames containing representations of target elements in stored medical video data before the operator needs to review the data. This preliminary processing makes previously unavailable information accessible without requiring the operator to manually search through entire datasets, thus improving reliability while maintaining ease of operation.
Solution Approach 2:
A computer-implemented system acts as an intermediary between the stored medical video data and the operator. The system processes the video data to identify target frames and presents only the relevant information to the operator, serving as a mediator that bridges the gap between complete data availability and operational simplicity.
2Device complexity
If stored medical video data is not processed to identify target frames, then the system is simpler and requires less processing power, but the quantity of available information is reduced
Solution Approach 1:
The system segments the stored medical video data into individual frames and processes only the necessary frames to identify target elements. This segmentation approach allows the system to manage large volumes of data without requiring processing of every frame simultaneously, reducing device complexity while increasing the quantity of extractable information.
Solution Approach 2:
The system extracts and identifies specific target frames from the stored medical video data that contain representations of target elements. By taking out only the relevant frames rather than processing the entire dataset, the system reduces processing requirements while making valuable information available to the operator.
3Quantity of substance
If all stored medical video data is made available to the operator, then the quantity of information is maximized, but the ease of operation deteriorates due to the overwhelming amount of data
Solution Approach 1:
The system extracts and identifies only the target frames containing representations of target elements from the stored medical video data. This extraction process filters out irrelevant information and presents only the necessary data to the operator, maximizing the quantity of useful information while maintaining ease of operation by avoiding data overload.
Solution Approach 2:
The system uses feedback from the operator's needs and the identified target elements to dynamically control what information is presented. By continuously feedback-driven selection of relevant frames, the system ensures that the maximum quantity of useful information is made available while adapting to operator requirements to maintain ease of operation.
Data Source
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AI summary
A mechanism for identifying one or more target frames containing a representation of a target element. An indicator of the target element is received and used to identify the target frame(s) within stored medical video data