Optoelectronic Blood Analysis With Absorption Curve Selection
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Solution Overview
Problem
Existing systems for measuring erythrocyte sedimentation rate (ESR) in blood samples face challenges in obtaining high-quality absorption curves free of artifacts and irregularities, which are crucial for accurate analysis.
Innovation Solution
A system that utilizes an optoelectronic unit to acquire multiple absorption curves under varying conditions, a processing unit to select an optimal curve based on predefined features, and perform comparisons using machine learning techniques to estimate ESR parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple absorption curves are acquired under different conditions, then the quality and reliability of the analysis is improved, but the measurement time and complexity increase
Solution Approach 1:
The system performs preliminary actions by acquiring multiple absorption curves under different conditions before final analysis. The processing unit selects the optimal curve from these pre-acquired curves, ensuring high-quality analysis without requiring repeated measurements during the actual analysis phase.
Solution Approach 2:
The system changes parameters such as radiation emission and detection parameters during different measurements. By varying these parameters across multiple curves and then selecting the optimal one, the system improves reliability while managing measurement time through intelligent parameter variation rather than repetitive measurements.
2Measurement precision
If automatic selection of optimal curves is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The processing unit performs self-service by automatically selecting the optimal absorption curve based on predefined criteria without requiring external intervention. This automation improves measurement precision while the selection algorithm itself manages the complexity internally, presenting a simple interface to the user.
Solution Approach 2:
The system implements feedback mechanisms where the processing unit evaluates multiple curves against predefined criteria and selects the optimal one. This feedback loop ensures precise measurements by continuously comparing curves and selecting the best match, managing complexity through structured evaluation protocols.
3Manufacturing precision
If reading curves are compared with reference models, then manufacturing precision of measurements is improved, but the time required for analysis increases
Solution Approach 1:
Reference curve models are prepared in advance and stored in the processing unit. When analysis is needed, the system quickly compares acquired curves against these pre-prepared references rather than performing complex real-time reference generation, improving both precision and speed.
Solution Approach 2:
The system creates copies of reference curve models that can be rapidly compared against acquired absorption curves. This copying approach allows for quick precision checks without requiring time-consuming real-time reference calculations, maintaining both accuracy and productivity.
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
Ensures the generation of high-quality reading curves, facilitating accurate ESR measurements by automatically selecting and optimizing the analysis process.
Implementation Method 1
at least one optoelectronic unit (4) configured to perform optical absorption measurements on a blood sample contained in a tube (P)
Data Source
AI summary
A blood sample analysis system having a detection unit for optical absorption measurements on a blood sample in a tube, moving means to cause movement between the detection unit and the tube, and a processing unit. The processing unit commands the execution by the detection unit of two distinct optical absorption measurements on the blood sample. The processing unit sets parameters of the detection unit to create for each distinct measurement a reading curve corresponding to the absorption of radiation emitted by the detection unit as a function of the relative movement between the detection unit and the tube and to perform a comparison of the distinct reading curves based on one or more references and to select a single reading curve, and to estimate measurement parameters, such as for the measurement of erythrocyte sedimentation rate, starting from the selected single reading curve.