Automatic Analyzer Abnormality Identification via Uncertainty Calculation
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Solution Overview
Problem
Automatic analyzers face challenges in identifying device-side abnormalities that affect measurement precision, making it difficult to maintain consistent and precise measurement values across clinical institutions, as the influence of components like the photometer, reagent dispenser, and stirrer is hard to determine, leading to time-consuming and laborious processes for laboratory technicians.
Innovation Solution
An automatic analyzer is configured with a detector, storage unit, calculator, display unit, and judgment unit to measure and analyze standard blood serums of multiple concentrations, calculate combined uncertainty estimates, and identify the cause of abnormalities, allowing for automated judgment and alerting of precision issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated uncertainty calculation and abnormality identification systems are implemented, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The analyzer performs self-diagnosis by automatically calculating uncertainty and identifying abnormality causes without requiring external intervention or complex manual analysis procedures
Solution Approach 2:
The system provides feedback by comparing measured values against calculated uncertainty thresholds and automatically identifying which components (photometer, dispenser, stirrer) are causing abnormalities, enabling closed-loop quality control
2Reliability
If comprehensive quality control analysis is performed to identify all abnormality causes, then reliability is improved, but loss of time increases
Solution Approach 1:
The system pre-calculates uncertainty values and establishes abnormality thresholds in advance, so that during actual measurement, the judgment unit can quickly compare and identify causes without time-consuming analysis
Solution Approach 2:
The automated judgment unit replaces manual analysis with algorithm-based automatic identification of abnormality causes, dramatically reducing the time required for quality control assessment
3Measurement precision
If detailed uncertainty analysis is conducted to identify specific component failures, then measurement precision is maintained, but ease of operation deteriorates
Solution Approach 1:
The analyzer automatically performs the complex task of identifying which specific component (photometer, dispenser syringe, stirrer) is causing measurement abnormalities, eliminating the need for operators to manually investigate each potential cause
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables timely and automated identification of analyzer abnormalities and consumable component deterioration, facilitating proper replacement timing and preventing precision decreases, thus ensuring consistent measurement values and reducing the burden on laboratory technicians.
Implementation Method 1
the measurement data are used to identify the causes of device-side abnormalities
Data Source
AI summary
Abnormality causes are automatically identified during daily quality control, based on the focused consideration of complex uncertainty factors and, especially, of the causes of device-side abnormalities, the latter of which are often difficult to identify. The analyzer performance that affects measurement results can be estimated from analysis parameters and calibration results. Thus, uncertainty estimates are automatically calculated for each analysis item during quality control, and the estimates are compared with uncertainties obtained during actual QC sample measurement, thereby monitoring and evaluating the analyzer performance. Also, measurements are performed on QC samples of multiple concentrations that contain substances known to subject to particular influences such as those of the optical system, sample dispenser, and reagent dispenser, so that the causes of abnormalities can be identified. Uncertainty estimates calculated from the parameters set for the analysis items are compared with uncertainties obtained from the QC sample measurements.