Rule Engine for Clinical Application Selection in Medical Imaging
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Solution Overview
Problem
Medical professionals face a cumbersome process when analyzing medical images, as they need to manually select appropriate clinical applications and data sets from multiple series, which can be time-consuming and often results in the selection of suboptimal data for specific applications.
Innovation Solution
A user interface is developed that utilizes a rule engine to analyze image data and identify suitable clinical applications and associated data sets, presenting them as icons for easy selection, allowing clinicians to streamline the analysis process by automating the selection of appropriate tools for image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a clinician manually reviews all series within a study to determine the best suited series for a particular application, then the clinician can select appropriate data sets for analysis, but the process becomes cumbersome and time-consuming, especially when a study includes a substantial number of series
Solution Approach 1:
The system performs preliminary analysis of the study data to pre-identify suitable clinical applications and their associated data sets before the clinician needs to make selections. The rule engine automatically executes application rules against the image data to determine which applications are appropriate and which data sets should be used, eliminating the need for the clinician to manually review all series.
Solution Approach 2:
The system serves itself by automatically identifying appropriate clinical applications and data sets without requiring manual intervention from the clinician. The rule engine autonomously analyzes the study characteristics and matches them with suitable applications, making the system self-configuring and self-optimizing for the specific medical imaging data being processed.
2Ease of operation
If the clinician manually selects series from multiple views or acquisitions, then the clinician has control over which data is used, but the complexity of the interface and process increases substantially
Solution Approach 1:
The system extracts and presents only the most relevant information to the clinician - specifically, the pre-identified clinical applications and their associated data sets. Instead of requiring the clinician to navigate through all possible series and make complex selections, the system extracts the essential choices and presents them in a simplified manner, reducing the effective complexity the clinician must manage.
Solution Approach 2:
The rule engine serves multiple functions simultaneously: it analyzes study characteristics, determines appropriate clinical applications, identifies suitable data sets, and presents recommendations to the clinician. This multi-functional approach consolidates what would otherwise require multiple separate tools or manual processes into a single integrated system.
Data Source
AI summary
A user interface for selecting clinical applications in a medical imaging system is provided on a display and is responsive to user inputs in the medical imaging system. A request to view a study that includes a plurality of images is received. The study to be viewed is then acquired from a medical imaging system database. The acquired study is analyzed with a rule engine that executes rules on image data from the acquired study. The rule engine identifies one or more clinical applications that are appropriate for the study and identifies at least one data set from the plurality of images suited for each of the identified one or more clinical applications. One or more icons each associated with one of the identified one or more clinical applications are displayed. The one or more icons are each selectable on the user interface to initialize the associated clinical application.