AI Classifier for Faint Test Line Detection in Diagnostic Devices
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Solution Overview
Problem
Home-based pregnancy test kits often lead to user confusion due to faint test lines, which can be misinterpreted as positive or negative results, undermining user confidence in the accuracy of the test results.
Innovation Solution
Implementing machine-based image detection within an artificial intelligence environment to recognize result indicators on diagnostic test kits, using a trained classifier to process digital images of the test sticks and provide accurate interpretations of test results, including faint or hard-to-see lines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user-based visual inspection is used to interpret test results, then the device complexity is low and ease of operation is high, but measurement precision deteriorates due to faint test lines being misinterpreted
Solution Approach 1:
The patent replaces the mechanical/visual inspection system with an image processing and machine learning system. A mobile device camera captures an image of the test stick, and an artificial intelligence model processes the image to detect and interpret test lines, substituting human visual inspection with automated digital image analysis to improve measurement precision
Solution Approach 2:
The patent introduces an intermediary image processing system between the test stick and the user. The system captures the test result through a camera, processes it through an AI model that analyzes pixel-level features, and presents the interpreted result to the user, acting as a mediator that enhances detection capability for faint test lines
2Reliability
If visual inspection methods are used, then the ease of operation is high, but reliability deteriorates due to user confusion and incorrect interpretation of faint lines
Solution Approach 1:
The patent replaces the unreliable human visual inspection system with an automated image processing system using machine learning. The system captures test stick images and uses trained AI models to objectively detect and interpret test lines, eliminating user confusion and improving reliability while maintaining ease of operation through automated processing
3Measurement precision
If machine-based image detection with AI classifier is implemented, then measurement precision improves for detecting faint test lines, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent leverages the multi-functionality of mobile devices, which already possess cameras, processors, and operating systems. By developing a mobile application that utilizes these existing components for image capture and AI-based analysis, the system achieves enhanced test line detection without requiring separate dedicated hardware, thus managing device complexity through resource sharing
Solution Approach 2:
The patent implements a self-service system where the mobile device's own camera, processor, and software capabilities are utilized to perform the image capture and analysis functions. The system serves itself by using the device's inherent resources rather than requiring external specialized equipment, balancing precision improvement with complexity management
Data Source
AI summary
Machine image detection involving a trained classifier is used to detect a result of a test stick diagnostic device configured to detect the presence of an analyte in a test sample. Example implementations include receiving a digital image comprising a depiction of at least a portion of a test stick diagnostic device and applying the digital image to a classifier configured to determine a relative position, relative orientation, and relative scale of the portion of the test stick diagnostic device with respect to the digital image, identify a test result region of the test stick diagnostic device, and detect a test result marking in the test result region of the test stick diagnostic device. An indication of a result of a diagnostic test may be provided based on the detected test result marking.