Machine Learning Drug Test Strip Analysis
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Solution Overview
Problem
Current semi-quantitative drug testing methods require samples to be transported to laboratories for analysis, preventing real-time interpretation at collection sites.
Innovation Solution
A computer-enabled system using machine learning to interpret semi-quantitative results from drug test strips, employing a photonic-enabled device to capture images of color indications and translate them into numerical concentration levels, with a database of machine learning models for evaluating drug test strips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If samples are transported to laboratories for semi-quantitative analysis, then accurate concentration levels can be determined, but real-time interpretation at collection sites cannot be achieved
Solution Approach 1:
The patent uses a photonic-enabled device to capture an optical image of the test strip, creating a digital copy that can be analyzed by machine learning algorithms. This digital replica allows semi-quantitative analysis to be performed locally without transporting the physical sample to a laboratory, thereby achieving both accurate concentration determination and real-time interpretation.
Solution Approach 2:
The patent replaces the mechanical/physical process of sample transport and laboratory analysis with an optical and computational system. A photonic device captures the test strip image, and machine learning algorithms process the optical data to determine concentration levels, eliminating the need for physical sample transport and enabling real-time results at the collection site.
2Productivity
If machine learning models are used to interpret test strip images, then real-time semi-quantitative results can be provided, but system complexity increases
Solution Approach 1:
The patent employs a universal machine learning model that can interpret multiple types of test strips for various analytes (drugs, hormones, medical conditions) using the same underlying technology platform. This multi-functional approach enables real-time semi-quantitative analysis across different applications without requiring separate complex systems for each test type, thereby managing system complexity while maintaining high 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
Enables real-time semi-quantitative test results at collection sites, providing accurate concentration levels of drugs in biological samples.
Implementation Method 1
Drug test strips used in drug testing have been impregnated with biochemical reagents that respond to the presence of drugs from a specific drug class. When there is sufficient concentration of drugs from the class present, the drug test strip will change color to indicate the presence of the drug in the sample.
Implementation Method 2
A portion of the sample to be tested is partially absorbed by the drug test strips, typically through capillary action.
Data Source
AI summary
A system and method for evaluating the concentration of an analyte contained on a diagnostic device. The method includes preparing a database of a plurality of digital images of drug test strips showing different concentrations of analytes; preparing a machine learning model of the drug test strips; comparing an active diagnostic device to the machine learning model, and evaluating the concentration of analytes In the active diagnostic device by comparing the active diagnostic device drug panel to the drug panel of the machine learning model of a diagnostic device.