Medical Measurement Data Post-Processing Method Selection
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Solution Overview
Problem
Current medical measurement data post-processing systems require manual selection of methods, leading to potential misuse of unsuitable methods and lack of guidance on optimal processing, resulting in suboptimal data handling.
Innovation Solution
A method that automatically selects the optimal post-processing method for medical measurement data by registering compatible components, acquiring context data, parsing the data, and evaluating it to determine the best-suited processing method, which can be prioritized and executed independently or with user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of post-processing methods is used, then user flexibility is maintained, but the risk of selecting unsuitable methods increases and processing efficiency decreases
Solution Approach 1:
The system automatically selects the appropriate post-processing method by evaluating the measurement data type and context without requiring manual user intervention. The computer autonomously determines which post-processing component is suitable based on the parsed data characteristics, eliminating the need for users to manually select methods while ensuring reliable and efficient processing.
2Productivity
If automatic post-processing method selection is implemented, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a registration module for storing post-processing components and their requirements, a parsing module for analyzing measurement data, and a selection module for automatically choosing the appropriate method. This segmentation manages system complexity by organizing functions into separate, manageable components that work together through defined interfaces.
Solution Approach 2:
A data structure serving as an intermediary is introduced to bridge the measurement data and the post-processing components. This intermediary structure standardizes the interface between the parsed data and the available processing methods, simplifying the automatic selection process while managing system complexity through a well-defined mediation layer.
3Measurement precision
If comprehensive context data acquisition is performed, then accuracy of method selection improves, but data processing time increases
Solution Approach 1:
Context data requirements are predetermined and prepared in advance through the registration of post-processing components. The system pre-defines what context information is needed for each type of measurement data, allowing the parsing and evaluation phases to efficiently retrieve and use this information without unnecessary delays during the actual selection process.
Data Source
AI summary
A method and a device are disclosed for selecting at least one post-processing method for the post-processing of medical measurement data. In this method, different post-processing components are registered. In addition to the measurement data, context data with respect to the measurement data are acquired and/or derived. Following this, a structured document is evaluated so that at least one post-processing method, for example an optimally designed method, can be selected for the respective measurement data.