Diagnostic Test Workflow Uncertainty Scoring for Result Accuracy
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Solution Overview
Problem
Diagnostic laboratory systems often produce inaccurate test results due to undetected operational failures during sequential testing processes, leading to potential misdiagnosis and inappropriate treatment.
Innovation Solution
A method involving a graph neural network that analyzes the workflow of diagnostic tests by converting operational measurements into vector representations, determining an uncertainty score based on collective analysis, and deciding whether to rerun the test based on this score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential testing operations are performed without comprehensive analysis, then testing efficiency is maintained, but test result accuracy deteriorates due to undetected operational failures
Solution Approach 1:
The patent combines multiple measurements from different operations into a unified analysis framework. The graph neural network integrates measurements from sample aspiration, reagent dispensing, incubation, and detection operations into a single test result accuracy assessment, allowing comprehensive evaluation while maintaining systematic organization through the workflow graph structure.
Solution Approach 2:
The patent introduces a graph neural network as an intermediary between raw operational measurements and final test results. This intermediary layer processes measurements, learns correlations between operations, and generates an uncertainty score that informs result interpretation, thereby improving accuracy without requiring direct modification of each operational step.
2Measurement precision
If comprehensive measurements from all operations are collected and analyzed, then test accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis by constructing the workflow graph and collecting measurements from all operations during test execution. The graph neural network is trained in advance to recognize patterns and correlations, enabling rapid uncertainty score generation without requiring extensive post-processing computation for each individual test.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based quality control methods with a graph neural network-based computational system. This substitution enables parallel processing of multiple measurements and automated pattern recognition, reducing processing time compared to manual analysis while maintaining comprehensive evaluation of all operational parameters.
3Reliability
If graph neural network analysis is implemented, then uncertainty detection capability is improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the testing process into distinct operations (sample aspiration, reagent dispensing, incubation, detection) and represents each as a separate node in the workflow graph. This segmentation allows the graph neural network to process each operation independently while capturing inter-operation correlations, making the complex analysis manageable and interpretable.
Solution Approach 2:
The patent implements a universal graph neural network framework that can analyze multiple different test types and operations through a single unified system. The workflow graph structure and measurement integration approach are applicable across various diagnostic tests, reducing implementation complexity compared to developing separate analysis systems for each test type.
Data Source
AI summary
A method of determining the accuracy of a test performed by a diagnostic laboratory system includes obtaining one or more first measurements during a first operation of the test performed by the diagnostic laboratory system. One or more second measurements are obtained during a second operation of the test performed by the diagnostic laboratory system. The first measurements and the second measurements are collectively analyzed using a trained model that calculates an uncertainty score for the test based on learned correlations between the first operation and the second operation. The uncertainty score may be used to determine whether the test results can be relied upon or whether the test should be rerun. Other methods and systems are disclosed.