CEUS Lesion Report Consistency Checking With Feedback Alerts
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Solution Overview
Problem
Inconsistencies in contrast-enhanced ultrasound (CEUS) reports can lead to incorrect diagnoses due to factors such as time-pressure and self-inconsistencies, which may result in poorer quality care and reduced patient outcomes.
Innovation Solution
A computer-implemented method and apparatus for processing CEUS images that compare properties in the report to a reference to detect inconsistencies, sending alerts to users for review and correction, and provide real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated assessment is used to generate CEUS reports, then productivity is improved, but reliability deteriorates due to inconsistencies between automated and manual measurements
Solution Approach 1:
The system performs retroactive discrepancy flagging by comparing automated assessment data with human assessment data, generating feedback alerts when inconsistencies are detected. This feedback loop allows for identification and correction of measurement discrepancies, thereby improving reliability while maintaining automated productivity.
Solution Approach 2:
The system introduces an intermediary consistency check mechanism that mediates between automated and manual assessments. This intermediary layer compares properties from both sources and flags discrepancies, serving as a bridge to ensure reliability without eliminating the benefits of automated productivity.
2Reliability
If manual assessment is used to ensure accuracy, then reliability is improved, but productivity deteriorates due to time-consuming review processes
Solution Approach 1:
Instead of requiring complete manual review of all CEUS reports, the system applies partial manual verification only when discrepancies are detected. The automated system handles routine cases efficiently, while human reviewers focus only on flagged inconsistencies, maintaining both productivity and reliability.
Solution Approach 2:
The automated assessment system performs self-verification by comparing its own measurements against reference standards and previously established consistency criteria. This self-service capability maintains reliability without requiring constant manual intervention, preserving productivity.
3Reliability
If comprehensive consistency checking is performed on all report properties, then reliability is improved, but device complexity increases
Solution Approach 1:
The system extracts and flags only the specific properties that exhibit inconsistencies between automated and manual assessments, rather than performing comprehensive checking on all report properties. This selective approach maintains reliability for critical measurements while reducing processing complexity.
Solution Approach 2:
The consistency checking applies different levels of scrutiny to different properties based on their clinical importance. Critical properties such as lesion size and characteristics receive thorough verification, while less critical properties undergo simplified checking, optimizing the balance between reliability and complexity.
Data Source
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AI summary
According to an aspect, there is provided a computer implemented method (300) of processing a contrast-enhanced ultrasound image of a lesion. The method comprises comparing (302) properties of a lesion as described in a contrast-enhanced ultrasound report of the contrast-enhanced ultrasound image to a reference in order to determine an inconsistency in the contrast-enhanced ultrasound report; and sending (304) a message to a user device to cause the user device to provide an alert relating to the inconsistency, to provide feedback.