Radiology Dose Benchmarking With Causal Graph Analysis
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Solution Overview
Problem
Existing radiation dose management systems require manual sorting and comparison of studies to identify similar variables for dose adjustment, lacking automation in identifying and adjusting factors influencing patient radiation exposure.
Innovation Solution
A medical system with a graphical user interface and analytics tool that automatically compares radiation dose variables, generates causal graphs, and adjusts parameters to optimize radiation delivery based on user input, reducing manual workload and enhancing dose management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sorting and comparison of data is used to identify differences in radiation dose among studies, then the user can explore factors impacting radiation dose, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of sorting and comparing radiation dose data with an automated computer-based system. The system automatically retrieves radiation dose data from multiple sources, compares the data using computational algorithms, and generates visualizations of factors impacting radiation dose, eliminating the need for manual data processing while maintaining comparison accuracy.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between raw radiation dose data and user interpretation. This intermediary system performs data retrieval, comparison, and visualization functions, acting as a mediator that processes the data automatically and presents results in an interpretable format, thereby reducing both time consumption and manual effort.
2Loss of information
If manual searching for parameters explaining radiation dose differences is performed, then the user can identify influential study variables, but the process lacks efficiency and automation
Solution Approach 1:
The patent replaces manual searching for influential parameters with automated computational analysis. The system uses computer algorithms to analyze radiation dose data, automatically identify study variables that have significant impact on radiation dose differences, and present these findings through visualizations, thereby maintaining thorough variable identification while dramatically improving analysis efficiency.
Solution Approach 2:
The patent enables the system to perform self-service analysis by automatically retrieving data, comparing radiation doses, identifying influential variables, and generating visualizations without requiring manual intervention at each step. The automated system serves itself by completing the entire analysis workflow from data retrieval to interpretation, significantly enhancing productivity.
3Reliability
If attempts to adjust radiation dose involve manually adjusting parameters and repeating scans, then the radiation dose can be optimized, but the process increases workload and time consumption
Solution Approach 1:
The patent applies preliminary action by automatically analyzing radiation dose data and identifying optimal parameter adjustments before actual scans are performed. The system uses historical and current data to predict which parameter changes will achieve desired radiation dose levels, allowing users to make informed adjustments without needing to perform multiple trial scans, thereby maintaining dose optimization reliability while improving efficiency.
Data Source
AI summary
Various methods and systems are provided for a medical system, comprising a client device having a graphical user interface (GUI) and a display device, the client device operably coupled to a network, and an analytics tool configured with instructions stored on a memory and executable by a processor to receive a first user input via the client device, identify values of at least one variable of interest (VOI) from a subject of a benchmark target variable which differ by at least a first threshold amount from other values of the same VOI from other subjects of the benchmark target variable within a benchmarking context, generate and output for display on the display device a causal graph illustrating influences of different study variables on an identified VOI value, and automatically update the causal graph and display thereof based on a second user input to adjust at least one study variable.