Conditional Protocol Exceptions for Medical Imaging Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Intelligent protocoling systems sometimes recommend incorrect medical imaging protocols due to unrepresented changes in medical practice or rare/idiosyncratic situations not captured in the training data, leading to inaccurate recommendations.
Innovation Solution
A computerized tool that executes a trained machine learning model and determines if conditional protocol exceptions are triggered, allowing for the selection of alternative medical imaging protocols when specific conditions are met, thereby addressing situations not captured in the training data or too rare to be confidently learned.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If intelligent protocoling uses trained machine learning models to recommend medical imaging protocols, then overall accuracy is improved, but incorrect recommendations occur in situations not captured in training data or too rare to be confidently learned
Solution Approach 1:
The system performs preliminary actions by checking whether patient data triggers any conditional protocol exceptions before finalizing the protocol recommendation. This preliminary check allows the system to proactively identify and handle rare or unrepresented situations that the machine learning model may not have confidently learned, thereby improving reliability without sacrificing adaptability.
Solution Approach 2:
The system introduces an intermediary component (the exception checking mechanism) that mediates between the machine learning model's recommendation and the final protocol selection. This intermediary layer evaluates whether the recommendation falls into rare or unrepresented categories by checking against conditional exceptions, allowing the system to maintain high accuracy while adapting to edge cases that the model alone cannot handle.
2Extent of automation
If the system always follows machine learning model recommendations, then automation is maximized, but accuracy decreases in edge cases not represented in training data
Solution Approach 1:
The system implements feedback by checking the machine learning model's recommendation against conditional protocol exceptions. When a recommendation triggers an exception (indicating a rare or unrepresented situation), the system provides feedback by selecting an alternative protocol instead of blindly following the model's recommendation. This feedback mechanism maintains high automation while improving accuracy in edge cases.
Solution Approach 2:
Before finalizing the automated protocol selection, the system performs a preliminary action of checking whether the recommendation triggers any conditional exceptions. This preliminary verification step ensures that automated selections are accurate even in edge cases, preventing incorrect recommendations while maintaining the benefits of automation.
3Reliability
If the system checks for conditional protocol exceptions, then accuracy in rare situations is improved, but device complexity increases
Solution Approach 1:
The system applies segmentation by dividing the protocol selection process into distinct components: the machine learning model generates recommendations, and a separate exception checking mechanism evaluates whether those recommendations are appropriate for rare or unrepresented situations. This segmentation allows each component to specialize, improving accuracy in edge cases without significantly increasing overall system complexity.
Solution Approach 2:
The exception checking mechanism serves as an intermediary layer between the machine learning model and the final protocol selection. This intermediary component adds minimal complexity by simply evaluating whether recommendations trigger predefined conditional exceptions, while significantly improving reliability in rare situations. The intermediary approach avoids the need for complete system redesign.
Data Source
AI summary
Systems/techniques that facilitate customized exceptions for intelligent protocoling are provided. In various embodiments, a system can access a data candidate associated with a medical patient. In various aspects, the system can execute a trained machine learning model on the data candidate, thereby identifying a first medical imaging protocol to be performed by a medical imaging scanner on the medical patient. In various instances, the system can determine whether the data candidate triggers a conditional protocol exception, wherein the conditional protocol exception identifies a second medical imaging protocol that is to be implemented when a condition is satisfied by the data candidate. In various cases, the system can, in response to determining that the data candidate satisfies the condition, select the second medical imaging protocol to be performed by the medical imaging scanner on the medical patient instead of selecting the first medical imaging protocol.