MIC Recognition Using Siamese Image Similarity for Rapid Antibiotic Testing
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Solution Overview
Problem
Conventional methods for determining minimum inhibitory concentration (MIC) of antibiotics are time-consuming and costly, failing to meet the requirements for rapid and accurate clinical diagnosis.
Innovation Solution
A method utilizing lensless imaging and a Siamese neural network to analyze images of bacterial strains with varying antibiotic concentrations, calculating similarity between testing and control images to determine MIC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated instrument detection method is used, then detection time is reduced to 10-12 hours, but detection cost increases and still fails to meet rapid diagnosis requirements
Solution Approach 1:
The patent uses image copying and processing techniques where testing images are captured and compared against control images. The Siamese neural network processes image pairs to determine MIC, replacing expensive automated instruments with a cost-effective image-based copying and comparison approach that achieves rapid detection within 6-8 hours.
2Measurement precision
If broth dilution method is used, then detection accuracy is maintained, but detection time increases to 24-48 hours which is unfavorable for clinical use
Solution Approach 1:
The patent replaces the mechanical broth dilution method with an optical imaging system. Instead of manually processing broth samples over 24-48 hours, the system captures optical images of the test plate and uses computer vision with Siamese neural networks to rapidly analyze bacterial growth inhibition, maintaining accuracy while reducing detection time to 6-8 hours.
Solution Approach 2:
The patent changes the detection parameter from measuring bacterial growth in broth to analyzing optical image characteristics. By transforming the detection approach from biochemical measurement to image-based measurement, the system achieves both rapid processing and accurate MIC determination through comparing testing images with control images.
3Reliability
If conventional detection methods are used, then reliable MIC determination is achieved, but operational complexity and time consumption increase
Solution Approach 1:
The patent implements self-service through automated image processing. The system automatically captures images, processes them through the Siamese neural network, and determines MIC values without requiring manual intervention. The automated instrument performs the entire detection process independently, improving ease of operation while maintaining reliable MIC determination through consistent algorithmic processing.
Solution Approach 2:
The patent uses feedback mechanisms where the Siamese neural network compares testing images with control images to determine similarity. This feedback loop allows the system to automatically adjust and refine MIC determination based on image comparison results, ensuring reliable outcomes while simplifying operation through automated decision-making.
Data Source
AI summary
A minimum inhibitory concentration recognition method, apparatus and device, and a computer-readable storage medium, relating to the field of image processing calculation. The method comprises: acquiring images under test of a strain under test; acquiring test contrast images corresponding to the strain under test; calculating the similarity between each image under test and the respective corresponding test contrast image to obtain a similarity test result respectively corresponding to each image under test; and according to the similarity test result and a preset gradient concentration, determining the minimum inhibitory concentration of an antibiotic under test corresponding to the strain under test. According to the present invention, the similarities between images under test of antibiotics-strains having different concentration gradients and the respective corresponding test contrast images can be calculated, so as to determine that the images under test are similar or dissimilar to the corresponding test contrast images, so that the minimum inhibitory concentration of the antibiotic under test corresponding to the strain under test is obtained, and the minimum inhibitory concentration of the antibiotic can be quickly and accurately measured and recognized, thereby reducing the measurement costs.