Automated Medical Image Processing Selection
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Solution Overview
Problem
Current medical imaging processing systems lack efficient automation for selecting and executing computerized advanced processing techniques, leading to inefficiencies in radiology workflows and delayed detection of critical findings.
Innovation Solution
A computing system that accesses medical images, determines relevant exam characteristics, and automatically initiates appropriate computerized advanced processing techniques based on predefined rules, enabling seamless integration with existing imaging systems and alerting mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual selection and execution of computerized advanced processing techniques is used, then radiologists have control over processing, but workflow efficiency decreases and time consumption increases
Solution Approach 1:
The system automatically determines exam characteristics and selects appropriate computerized advanced processing techniques without requiring manual radiologist intervention. The processing system serves itself by autonomously accessing image data, evaluating exam parameters, and initiating relevant processing workflows based on predefined criteria and rules.
Solution Approach 2:
The system pre-establishes rules and criteria for selecting computerized advanced processing techniques based on exam characteristics. By having processing decisions predetermined through configured rules, the system eliminates real-time manual decision-making and enables automatic execution when images are uploaded or processed.
2Productivity
If automated processing selection is implemented, then workflow efficiency improves, but system complexity increases
Solution Approach 1:
The automated processing system is divided into distinct functional modules: an image data access component that retrieves medical images, an exam characteristic determination component that evaluates image parameters, and a processing technique selection component that applies rules to select appropriate processing. This segmentation manages complexity by creating independent, manageable modules with clear interfaces.
Solution Approach 2:
The system introduces an intermediary processing layer between image acquisition and final analysis that automatically evaluates exam characteristics and selects processing techniques. This intermediary component mediates between raw image data and advanced processing, using predefined rules to bridge the gap without requiring direct manual intervention or overly complex integrated systems.
3Reliability
If comprehensive processing techniques are applied to all images, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system applies different processing techniques selectively based on local characteristics of each exam. By evaluating specific exam parameters and matching them with appropriate processing methods through configured rules, the system ensures that each image receives the specific processing needed for its particular characteristics rather than uniform comprehensive processing.
Solution Approach 2:
The system changes processing parameters and technique selection based on detected exam characteristics. By dynamically adjusting which processing techniques are applied according to the specific parameters of each exam type, the system optimizes the balance between detection accuracy and processing efficiency, applying comprehensive processing only when necessary.
Data Source
AI summary
Systems and methods are disclosed for automatically managing how and when computerized advanced processing techniques (for example, CAD and/or other image processing) are used. In some embodiments, the systems and methods discussed herein allow users, such as radiologists, to efficiently interact with a wide variety of computerized advanced processing (“CAP”) techniques using computing devices ranging from picture archiving and communication system (“PACS”) workstations to handheld devices such as smartphone and tablets. Furthermore, the systems and methods may, in various embodiments, automatically manage how data associated with these CAP techniques (for example, results of application of one or more computerized advanced processing techniques) are used, such as how data associated with the computerized analyses is reported, whether comparisons to prior abnormalities should be automatically initiated, whether the radiologist should be alerted of important findings, and the like.