Clot Waveform Normalization for Blood Clotting Cause Estimation
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Solution Overview
Problem
Existing methods for identifying the cause of prolonged blood clotting time, such as cross mixing tests and clot waveform analysis, are burdensome for examiners and patients, require additional blood samples, and lack quantitative accuracy due to variable clot waveform lengths.
Innovation Solution
A method using a neural network to estimate the cause of prolonged blood clotting time by preprocessing clot waveforms with variable data lengths through normalization and differentiation, followed by feature extraction and alignment, enabling accurate identification of FVIII-deficient, FIX-deficient, and LA-positive samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross mixing test is performed to identify cause of prolongation, then diagnostic accuracy is improved, but examination complexity and burden on examiner increase
Solution Approach 1:
The patent replaces the manual cross-mixing test procedure with an automated image processing system. The analyzer captures images of clot formation and uses computer vision algorithms to automatically determine the cause of prolongation, substituting the examiner's manual mixing and visual assessment with automated optical detection and image analysis.
Solution Approach 2:
The system enables self-service diagnosis by automatically analyzing clot formation images without requiring examiner intervention. The image processing algorithm independently identifies the cause of prolongation by comparing clot formation patterns against reference data, eliminating the need for examiner expertise in interpreting cross-mixing results.
2Measurement precision
If cross mixing test is performed to identify cause of prolongation, then diagnostic accuracy is improved, but burden on patient increases due to additional blood collection
Solution Approach 1:
The patent extracts the essential diagnostic information from a single blood sample by analyzing the clot formation process itself, rather than requiring multiple separate blood collections for cross-mixing tests. The image processing system extracts diagnostic features from the temporal and spatial characteristics of clot formation in the captured images.
3Ease of operation
If clot waveform analysis is used to reduce burden, then ease of operation is improved, but measurement precision deteriorates due to variable waveform lengths
Solution Approach 1:
The patent employs dynamic image processing that adapts to varying clot formation speeds and patterns. The system captures images at multiple time points and uses algorithms that can handle variable-duration clot formation processes, adjusting the analysis based on the actual clotting kinetics observed in each sample.
Solution Approach 2:
The patent transitions from analyzing one-dimensional clot waveforms to processing two-dimensional images with spatial and temporal information. By capturing images of clot formation and analyzing spatial patterns of clot propagation, the system adds dimensional information that improves precision while maintaining ease of operation.
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
Enables precise identification of clotting time causes with reduced burden on patients and examiners, using a neural network to analyze clot waveforms with variable lengths, improving diagnostic accuracy for hemophilia and anti-phospholipid antibody syndrome.
Implementation Method 1
The target clot waveform is a waveform acquired by measuring light, in which a change in turbidity over time is recorded as a fibrin clot is formed
Data Source
AI summary
A cause of prolongation of a blood clotting time for a test sample is estimated by acquiring a clot waveform showing a change in light amount over time according to a coagulation reaction of a reaction liquid. A first waveform is acquired by performing before-after differentiation processing on the clot waveform. First and second fitted waveforms are acquired by performing fitting processing on the clot waveform and the first waveform. A third waveform is acquired by performing before-after differentiation processing on the second fitted waveform. First to third normalized waveforms are acquired by normalizing the first and second fitted waveforms and the third waveform, respectively, on a light amount axis and a time axis. A feature from each of the first to third normalized waveforms is extracted; and the cause of prolongation of a blood clotting time is estimated based on a known feature and the extracted feature.