Automated Assay Detection in Microfluidic Devices
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Solution Overview
Problem
Current microfluidic devices lack efficient methods for automated detection of assay-positive areas, particularly in identifying and quantifying analytes within microfluidic channels and sequestration pens, which hinders precise analysis and data interpretation.
Innovation Solution
An automated method that involves identifying assay areas based on the dimensions of microfluidic channels and sequestration pens, collecting digital images, calculating the rate of change of parameters such as light intensity, and comparing it to a threshold to determine assay positivity, along with a machine-readable storage device storing instructions for these processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection methods are implemented in microfluidic devices, then productivity and measurement precision are improved, but device complexity increases
Solution Approach 1:
The imaging device is configured to perform multiple functions: capturing images of microfluidic channels, identifying assay areas based on circuit element dimensions, and calculating rate of change parameters. This multi-functionality improves productivity without requiring separate dedicated devices for each task, thereby limiting the increase in device complexity.
Solution Approach 2:
The system automatically identifies assay areas based on the known dimensions of circuit elements (channels, sequestration pens) and autonomously calculates rate of change parameters from captured images. This self-service capability eliminates the need for manual area selection and parameter calculation, significantly improving productivity while the automation is integrated into the existing imaging system, limiting complexity increase.
2Measurement precision
If manual identification of assay areas is used, then device complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The system is pre-programmed with the dimensions of circuit elements (channel width, sequestration pen dimensions) before the assay begins. During image analysis, these pre-stored dimensional parameters are automatically applied to identify assay areas, ensuring consistent and precise identification without manual intervention. This preliminary preparation enables high measurement precision while the automation is built into the software, limiting the perceived complexity increase.
Solution Approach 2:
The system captures images, automatically identifies assay areas based on circuit element dimensions, calculates rate of change parameters, and compares results to thresholds to determine positivity. This closed-loop feedback process continuously refines measurements, improving precision. The feedback is handled by software algorithms integrated with the imaging device, which limits complexity increase compared to manual methods.
3Productivity
If automated rate of change calculation is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system calculates rate of change parameters only for the automatically identified assay areas within the microfluidic channels and sequestration pens, rather than processing entire images. This partial action approach focuses computational resources on relevant regions, improving productivity by quickly processing only necessary data while limiting energy consumption by avoiding unnecessary calculations in non-assay areas.
Data Source
Figure 1
Figure 2A~2B
Figure 2C~2D
AI summary
A method is provided for the automated detection of assay-positive assay areas in a microfluidic device comprising one or more circuit elements, the method comprising collecting a set of digital images of an automatically-identified assay area (570, 572), wherein the automatically-identified assay area is identified based, at least in part, on the dimensions of the one or more circuit elements (522), calculating a rate of change over the course of all or part of the assay based on the set of digital images of the automatically-identified assay area, comparing the rate of change to a threshold value, and determining that the automatically-identified assay region is assay-positive if the rate of change is greater than the threshold value.